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AashishTiwari/N-Gram_NewsAPI_Org.ipynb

Created March 24, 2017 18:35
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N-Gram_NewsAPI_Org.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Modern NLP on NewsAPI dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Credits:\n",
"(http://pydata.org/dc2016/schedule/presentation/11/). To view the video of the presentation on YouTube, see [here](https://www.youtube.com/watch?v=6zm9NC9uRkk)._"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Our Trail Map\n",
"An end-to-end data science & natural language processing pipeline, starting with **raw data** and running through **preparing**, **modeling**, **visualizing**, and **analyzing** the data. We'll touch on the following points:\n",
"1. A tour of the dataset\n",
"1. Introduction to text processing with spaCy\n",
"1. Automatic phrase modeling\n",
"1. Topic modeling with LDA\n",
"1. Visualizing topic models with pyLDAvis\n",
"1. Word vector models with word2vec\n",
"1. Visualizing word2vec with t-SNE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The NewsAPI Org Dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"author,description,publishedAt,source,title,url,urlToImage,category,scraping_date\n",
"\n"
]
}
],
"source": [
"import os\n",
"import codecs\n",
"\n",
"data_directory = os.path.join('post2')\n",
"\n",
"businesses_filepath = os.path.join(data_directory,\n",
" 'news.csv')\n",
"\n",
"with codecs.open(businesses_filepath, encoding='utf_8') as f:\n",
" first_business_record = f.readline() \n",
"\n",
"print(first_business_record)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['general' 'technology' 'sport' 'business' 'entertainment' 'gaming' 'music'\n",
" 'science-and-nature']\n"
]
}
],
"source": [
"import pandas as pd\n",
"df = pd.read_csv(businesses_filepath)\n",
"categories = df.category.unique()\n",
"print(categories)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"intermediate_directory = os.path.join('d:', 'news_api_intermediate')\n",
"\n",
"desc_txt_filepath = os.path.join(intermediate_directory,\n",
" 'news_desc_all.txt')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df1 = pd.DataFrame(df.description)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df1.to_csv(desc_txt_filepath, header=None, index=None, sep=' ', encoding='utf-8')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## spaCy — Industrial-Strength NLP in Python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![spaCy](https://s3.amazonaws.com/skipgram-images/spaCy.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[**spaCy**](https://spacy.io) is an industrial-strength natural language processing (_NLP_) library for Python. spaCy's goal is to take recent advancements in natural language processing out of research papers and put them in the hands of users to build production software.\n",
"\n",
"spaCy handles many tasks commonly associated with building an end-to-end natural language processing pipeline:\n",
"- Tokenization\n",
"- Text normalization, such as lowercasing, stemming/lemmatization\n",
"- Part-of-speech tagging\n",
"- Syntactic dependency parsing\n",
"- Sentence boundary detection\n",
"- Named entity recognition and annotation\n",
"\n",
"In the \"batteries included\" Python tradition, spaCy contains built-in data and models which you can use out-of-the-box for processing general-purpose English language text:\n",
"- Large English vocabulary, including stopword lists\n",
"- Token \"probabilities\"\n",
"- Word vectors\n",
"\n",
"spaCy is written in optimized Cython, which means it's _fast_. According to a few independent sources, it's the fastest syntactic parser available in any language. Key pieces of the spaCy parsing pipeline are written in pure C, enabling efficient multithreading (i.e., spaCy can release the _GIL_)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import spacy\n",
"import pandas as pd\n",
"import itertools as it\n",
"\n",
"nlp = spacy.load('en')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's grab a sample review to play with."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\"Latest GTX 1060 laptop is more portable than its big ol’ butt might suggest.\"\r\n",
"\n"
]
}
],
"source": [
"with codecs.open(desc_txt_filepath, encoding='utf_8') as f:\n",
" sample_news = list(it.islice(f, 8, 9))[0]\n",
" sample_news = sample_news.replace('\\\\n', '\\n')\n",
" \n",
"print(sample_news)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hand the review text to spaCy, and be prepared to wait..."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 6 ms\n"
]
}
],
"source": [
"%%time\n",
"parsed_news = nlp(sample_news)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\"Latest GTX 1060 laptop is more portable than its big ol’ butt might suggest.\"\r\n",
"\n"
]
}
],
"source": [
"print(parsed_news)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks the same! What happened under the hood?\n",
"\n",
"What about sentence detection and segmentation?"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sentence 1:\n",
"\"Latest GTX 1060 laptop is more portable than its big ol’ butt might suggest.\"\r\n",
"\n",
"\n"
]
}
],
"source": [
"for num, sentence in enumerate(parsed_news.sents):\n",
" print('Sentence {}:'.format(num + 1))\n",
" print(sentence)\n",
" print('')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What about named entity detection?"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"for num, entity in enumerate(parsed_news.ents):\n",
" print('Entity {}:'.format(num + 1), entity, '-', entity.label_)\n",
" print('')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What about part of speech tagging?"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>token_text</th>\n",
" <th>part_of_speech</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>\"</td>\n",
" <td>PUNCT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Latest</td>\n",
" <td>ADJ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>GTX</td>\n",
" <td>PROPN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1060</td>\n",
" <td>NUM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>laptop</td>\n",
" <td>NOUN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>is</td>\n",
" <td>VERB</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>more</td>\n",
" <td>ADV</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>portable</td>\n",
" <td>ADJ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>than</td>\n",
" <td>ADP</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>its</td>\n",
" <td>ADJ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>big</td>\n",
" <td>ADJ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>ol’</td>\n",
" <td>ADJ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>butt</td>\n",
" <td>NOUN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>might</td>\n",
" <td>VERB</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>suggest</td>\n",
" <td>VERB</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>.</td>\n",
" <td>PUNCT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>\"</td>\n",
" <td>PUNCT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>\\r\\n</td>\n",
" <td>SPACE</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" token_text part_of_speech\n",
"0 \" PUNCT\n",
"1 Latest ADJ\n",
"2 GTX PROPN\n",
"3 1060 NUM\n",
"4 laptop NOUN\n",
"5 is VERB\n",
"6 more ADV\n",
"7 portable ADJ\n",
"8 than ADP\n",
"9 its ADJ\n",
"10 big ADJ\n",
"11 ol’ ADJ\n",
"12 butt NOUN\n",
"13 might VERB\n",
"14 suggest VERB\n",
"15 . PUNCT\n",
"16 \" PUNCT\n",
"17 \\r\\n SPACE"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"token_text = [token.orth_ for token in parsed_news]\n",
"token_pos = [token.pos_ for token in parsed_news]\n",
"\n",
"pd.DataFrame(list(zip(token_text, token_pos)),\n",
" columns=['token_text', 'part_of_speech'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What about text normalization, like stemming/lemmatization and shape analysis?"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>token_text</th>\n",
" <th>token_lemma</th>\n",
" <th>token_shape</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>\"</td>\n",
" <td>\"</td>\n",
" <td>\"</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Latest</td>\n",
" <td>late</td>\n",
" <td>Xxxxx</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>GTX</td>\n",
" <td>gtx</td>\n",
" <td>XXX</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1060</td>\n",
" <td>1060</td>\n",
" <td>dddd</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>laptop</td>\n",
" <td>laptop</td>\n",
" <td>xxxx</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>is</td>\n",
" <td>be</td>\n",
" <td>xx</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>more</td>\n",
" <td>more</td>\n",
" <td>xxxx</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>portable</td>\n",
" <td>portable</td>\n",
" <td>xxxx</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>than</td>\n",
" <td>than</td>\n",
" <td>xxxx</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>its</td>\n",
" <td>its</td>\n",
" <td>xxx</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>big</td>\n",
" <td>big</td>\n",
" <td>xxx</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>ol’</td>\n",
" <td>ol’</td>\n",
" <td>xx’</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>butt</td>\n",
" <td>butt</td>\n",
" <td>xxxx</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>might</td>\n",
" <td>may</td>\n",
" <td>xxxx</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>suggest</td>\n",
" <td>suggest</td>\n",
" <td>xxxx</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>.</td>\n",
" <td>.</td>\n",
" <td>.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>\"</td>\n",
" <td>\"</td>\n",
" <td>\"</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>\\r\\n</td>\n",
" <td>\\r\\n</td>\n",
" <td>\\r\\n</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" token_text token_lemma token_shape\n",
"0 \" \" \"\n",
"1 Latest late Xxxxx\n",
"2 GTX gtx XXX\n",
"3 1060 1060 dddd\n",
"4 laptop laptop xxxx\n",
"5 is be xx\n",
"6 more more xxxx\n",
"7 portable portable xxxx\n",
"8 than than xxxx\n",
"9 its its xxx\n",
"10 big big xxx\n",
"11 ol’ ol’ xx’\n",
"12 butt butt xxxx\n",
"13 might may xxxx\n",
"14 suggest suggest xxxx\n",
"15 . . .\n",
"16 \" \" \"\n",
"17 \\r\\n \\r\\n \\r\\n"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"token_lemma = [token.lemma_ for token in parsed_news]\n",
"token_shape = [token.shape_ for token in parsed_news]\n",
"\n",
"pd.DataFrame(list(zip(token_text, token_lemma, token_shape)),\n",
" columns=['token_text', 'token_lemma', 'token_shape'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What about token-level entity analysis?"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>token_text</th>\n",
" <th>entity_type</th>\n",
" <th>inside_outside_begin</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>\"</td>\n",
" <td></td>\n",
" <td>O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Latest</td>\n",
" <td></td>\n",
" <td>O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>GTX</td>\n",
" <td></td>\n",
" <td>O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1060</td>\n",
" <td></td>\n",
" <td>O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>laptop</td>\n",
" <td></td>\n",
" <td>O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>is</td>\n",
" <td></td>\n",
" <td>O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>more</td>\n",
" <td></td>\n",
" <td>O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>portable</td>\n",
" <td></td>\n",
" <td>O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>than</td>\n",
" <td></td>\n",
" <td>O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>its</td>\n",
" <td></td>\n",
" <td>O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>big</td>\n",
" <td></td>\n",
" <td>O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>ol’</td>\n",
" <td></td>\n",
" <td>O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>butt</td>\n",
" <td></td>\n",
" <td>O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>might</td>\n",
" <td></td>\n",
" <td>O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>suggest</td>\n",
" <td></td>\n",
" <td>O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>.</td>\n",
" <td></td>\n",
" <td>O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>\"</td>\n",
" <td></td>\n",
" <td>O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>\\r\\n</td>\n",
" <td></td>\n",
" <td>O</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" token_text entity_type inside_outside_begin\n",
"0 \" O\n",
"1 Latest O\n",
"2 GTX O\n",
"3 1060 O\n",
"4 laptop O\n",
"5 is O\n",
"6 more O\n",
"7 portable O\n",
"8 than O\n",
"9 its O\n",
"10 big O\n",
"11 ol’ O\n",
"12 butt O\n",
"13 might O\n",
"14 suggest O\n",
"15 . O\n",
"16 \" O\n",
"17 \\r\\n O"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"token_entity_type = [token.ent_type_ for token in parsed_news]\n",
"token_entity_iob = [token.ent_iob_ for token in parsed_news]\n",
"\n",
"pd.DataFrame(list(zip(token_text, token_entity_type, token_entity_iob)),\n",
" columns=['token_text', 'entity_type', 'inside_outside_begin'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What about a variety of other token-level attributes, such as the relative frequency of tokens, and whether or not a token matches any of these categories?\n",
"- stopword\n",
"- punctuation\n",
"- whitespace\n",
"- represents a number\n",
"- whether or not the token is included in spaCy's default vocabulary?"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>text</th>\n",
" <th>log_probability</th>\n",
" <th>stop?</th>\n",
" <th>punctuation?</th>\n",
" <th>whitespace?</th>\n",
" <th>number?</th>\n",
" <th>out of vocab.?</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>\"</td>\n",
" <td>-5.026776</td>\n",
" <td></td>\n",
" <td>Yes</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Latest</td>\n",
" <td>-14.270922</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>GTX</td>\n",
" <td>-11.719471</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1060</td>\n",
" <td>-16.522951</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>Yes</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>laptop</td>\n",
" <td>-10.292619</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>is</td>\n",
" <td>-4.457749</td>\n",
" <td>Yes</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>more</td>\n",
" <td>-6.081598</td>\n",
" <td>Yes</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>portable</td>\n",
" <td>-12.061860</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>than</td>\n",
" <td>-6.512254</td>\n",
" <td>Yes</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>its</td>\n",
" <td>-7.321458</td>\n",
" <td>Yes</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>big</td>\n",
" <td>-7.880736</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>ol’</td>\n",
" <td>-19.502029</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>butt</td>\n",
" <td>-10.587015</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>might</td>\n",
" <td>-7.490108</td>\n",
" <td>Yes</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>suggest</td>\n",
" <td>-9.402390</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>.</td>\n",
" <td>-3.067898</td>\n",
" <td></td>\n",
" <td>Yes</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>\"</td>\n",
" <td>-5.026776</td>\n",
" <td></td>\n",
" <td>Yes</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>\\r\\n</td>\n",
" <td>-10.518562</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>Yes</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" text log_probability stop? punctuation? whitespace? number? \\\n",
"0 \" -5.026776 Yes \n",
"1 Latest -14.270922 \n",
"2 GTX -11.719471 \n",
"3 1060 -16.522951 Yes \n",
"4 laptop -10.292619 \n",
"5 is -4.457749 Yes \n",
"6 more -6.081598 Yes \n",
"7 portable -12.061860 \n",
"8 than -6.512254 Yes \n",
"9 its -7.321458 Yes \n",
"10 big -7.880736 \n",
"11 ol’ -19.502029 \n",
"12 butt -10.587015 \n",
"13 might -7.490108 Yes \n",
"14 suggest -9.402390 \n",
"15 . -3.067898 Yes \n",
"16 \" -5.026776 Yes \n",
"17 \\r\\n -10.518562 Yes \n",
"\n",
" out of vocab.? \n",
"0 \n",
"1 \n",
"2 \n",
"3 \n",
"4 \n",
"5 \n",
"6 \n",
"7 \n",
"8 \n",
"9 \n",
"10 \n",
"11 Yes \n",
"12 \n",
"13 \n",
"14 \n",
"15 \n",
"16 \n",
"17 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"token_attributes = [(token.orth_,\n",
" token.prob,\n",
" token.is_stop,\n",
" token.is_punct,\n",
" token.is_space,\n",
" token.like_num,\n",
" token.is_oov)\n",
" for token in parsed_news]\n",
"\n",
"df = pd.DataFrame(token_attributes,\n",
" columns=['text',\n",
" 'log_probability',\n",
" 'stop?',\n",
" 'punctuation?',\n",
" 'whitespace?',\n",
" 'number?',\n",
" 'out of vocab.?'])\n",
"\n",
"df.loc[:, 'stop?':'out of vocab.?'] = (df.loc[:, 'stop?':'out of vocab.?']\n",
" .applymap(lambda x: 'Yes' if x else ''))\n",
" \n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If the text you'd like to process is general-purpose English language text (i.e., not domain-specific, like medical literature), spaCy is ready to use out-of-the-box.\n",
"\n",
"I think it will eventually become a core part of the Python data science ecosystem &mdash; it will do for natural language computing what other great libraries have done for numerical computing."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Phrase Modeling"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_Phrase modeling_ is another approach to learning combinations of tokens that together represent meaningful multi-word concepts. We can develop phrase models by looping over the the words in our reviews and looking for words that _co-occur_ (i.e., appear one after another) together much more frequently than you would expect them to by random chance. The formula our phrase models will use to determine whether two tokens $A$ and $B$ constitute a phrase is:\n",
"\n",
"$$\\frac{count(A\\ B) - count_{min}}{count(A) * count(B)} * N > threshold$$\n",
"\n",
"...where:\n",
"* $count(A)$ is the number of times token $A$ appears in the corpus\n",
"* $count(B)$ is the number of times token $B$ appears in the corpus\n",
"* $count(A\\ B)$ is the number of times the tokens $A\\ B$ appear in the corpus *in order*\n",
"* $N$ is the total size of the corpus vocabulary\n",
"* $count_{min}$ is a user-defined parameter to ensure that accepted phrases occur a minimum number of times\n",
"* $threshold$ is a user-defined parameter to control how strong of a relationship between two tokens the model requires before accepting them as a phrase\n",
"\n",
"Once our phrase model has been trained on our corpus, we can apply it to new text. When our model encounters two tokens in new text that identifies as a phrase, it will merge the two into a single new token.\n",
"\n",
"Phrase modeling is superficially similar to named entity detection in that you would expect named entities to become phrases in the model (so _new york_ would become *new\\_york*). But you would also expect multi-word expressions that represent common concepts, but aren't specifically named entities (such as _happy hour_) to also become phrases in the model.\n",
"\n",
"We turn to the indispensible [**gensim**](https://radimrehurek.com/gensim/index.html) library to help us with phrase modeling &mdash; the [**Phrases**](https://radimrehurek.com/gensim/models/phrases.html) class in particular."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\aashis_tiwari\\AppData\\Local\\Continuum\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\gensim\\utils.py:855: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
" warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n"
]
}
],
"source": [
"from gensim.models import Phrases\n",
"from gensim.models.word2vec import LineSentence"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we're performing phrase modeling, we'll be doing some iterative data transformation at the same time. Our roadmap for data preparation includes:\n",
"\n",
"1. Segment text of complete reviews into sentences & normalize text\n",
"1. First-order phrase modeling $\\rightarrow$ _apply first-order phrase model to transform sentences_\n",
"1. Second-order phrase modeling $\\rightarrow$ _apply second-order phrase model to transform sentences_\n",
"1. Apply text normalization and second-order phrase model to text of complete reviews\n",
"\n",
"We'll use this transformed data as the input for some higher-level modeling approaches in the following sections."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, let's define a few helper functions that we'll use for text normalization. In particular, the `lemmatized_sentence_corpus` generator function will use spaCy to:\n",
"- Iterate over the 1M reviews in the `review_txt_all.txt` we created before\n",
"- Segment the reviews into individual sentences\n",
"- Remove punctuation and excess whitespace\n",
"- Lemmatize the text\n",
"\n",
"... and do so efficiently in parallel, thanks to spaCy's `nlp.pipe()` function."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def punct_space(token):\n",
" \"\"\"\n",
" helper function to eliminate tokens\n",
" that are pure punctuation or whitespace\n",
" \"\"\"\n",
" \n",
" return token.is_punct or token.is_space\n",
"\n",
"def line_review(filename):\n",
" \"\"\"\n",
" generator function to read in reviews from the file\n",
" and un-escape the original line breaks in the text\n",
" \"\"\"\n",
" \n",
" with codecs.open(filename, encoding='utf_8') as f:\n",
" for review in f:\n",
" yield review.replace('\\\\n', '\\n')\n",
" \n",
"def lemmatized_sentence_corpus(filename):\n",
" \"\"\"\n",
" generator function to use spaCy to parse reviews,\n",
" lemmatize the text, and yield sentences\n",
" \"\"\"\n",
" \n",
" for parsed_reason in nlp.pipe(line_review(filename),\n",
" batch_size=10000, n_threads=4):\n",
" \n",
" for sent in parsed_reason.sents:\n",
" yield u' '.join([token.lemma_ for token in sent\n",
" if not punct_space(token)])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"unigram_sentences_filepath = os.path.join(intermediate_directory,\n",
" 'unigram_sentences_all.txt')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's use the `lemmatized_sentence_corpus` generator to loop over the original review text, segmenting the reviews into individual sentences and normalizing the text. We'll write this data back out to a new file (`unigram_sentences_all`), with one normalized sentence per line. We'll use this data for learning our phrase models."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 44.9 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# this is a bit time consuming - make the if statement True\n",
"# if you want to execute data prep yourself.\n",
"if 1 == 1:\n",
"\n",
" with codecs.open(unigram_sentences_filepath, 'w', encoding='utf_8') as f:\n",
" for sentence in lemmatized_sentence_corpus(desc_txt_filepath):\n",
" f.write(sentence + '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If your data is organized like our `unigram_sentences_all` file now is &mdash; a large text file with one document/sentence per line &mdash; gensim's [**LineSentence**](https://radimrehurek.com/gensim/models/word2vec.html#gensim.models.word2vec.LineSentence) class provides a convenient iterator for working with other gensim components. It *streams* the documents/sentences from disk, so that you never have to hold the entire corpus in RAM at once. This allows you to scale your modeling pipeline up to potentially very large corpora."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unigram_sentences = LineSentence(unigram_sentences_filepath)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a look at a few sample sentences in our new, transformed file."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"new orleans ap a man who allegedly plow into a crowd enjoy the krewe of endymion parade on saturday in the mid city section of new orleans be be investigate for drive while intoxicated police say\n",
"\n"
]
}
],
"source": [
"for unigram_sentence in it.islice(unigram_sentences, 24, 25):\n",
" print(' '.join(unigram_sentence))\n",
" print('')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we'll learn a phrase model that will link individual words into two-word phrases. We'd expect words that together represent a specific concept, like \"`ice cream`\", to be linked together to form a new, single token: \"`ice_cream`\"."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bigram_model_filepath = os.path.join(intermediate_directory, 'bigram_model_all')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 4.03 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# this is a bit time consuming - make the if statement True\n",
"# if you want to execute modeling yourself.\n",
"if 1 == 1:\n",
"\n",
" bigram_model = Phrases(unigram_sentences)\n",
"\n",
" bigram_model.save(bigram_model_filepath)\n",
" \n",
"# load the finished model from disk\n",
"bigram_model = Phrases.load(bigram_model_filepath)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have a trained phrase model for word pairs, let's apply it to the review sentences data and explore the results."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bigram_sentences_filepath = os.path.join(intermediate_directory,\n",
" 'bigram_sentences_all.txt')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\aashis_tiwari\\AppData\\Local\\Continuum\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\gensim\\models\\phrases.py:274: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
" warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 6.58 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# this is a bit time consuming - make the if statement True\n",
"# if you want to execute data prep yourself.\n",
"if 1 == 1:\n",
"\n",
" with codecs.open(bigram_sentences_filepath, 'w', encoding='utf_8') as f:\n",
" \n",
" for unigram_sentence in unigram_sentences:\n",
" \n",
" bigram_sentence = ' '.join(bigram_model[unigram_sentence])\n",
" \n",
" f.write(bigram_sentence + '\\n')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bigram_sentences = LineSentence(bigram_sentences_filepath)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"new_orleans ap a man_who allegedly plow_into a crowd enjoy the krewe of endymion parade on_saturday in the mid city section of new_orleans be be investigate for drive while intoxicated police_say\n",
"\n"
]
}
],
"source": [
"for bigram_sentence in it.islice(bigram_sentences, 24, 25):\n",
" print(' '.join(bigram_sentence))\n",
" print('')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks like the phrase modeling worked! We now see two-word phrases, such as \"`ice_cream`\" and \"`apple_pie`\", linked together in the text as a single token. Next, we'll train a _second-order_ phrase model. We'll apply the second-order phrase model on top of the already-transformed data, so that incomplete word combinations like \"`vanilla_ice cream`\" will become fully joined to \"`vanilla_ice_cream`\". No disrespect intended to [Vanilla Ice](https://www.youtube.com/watch?v=rog8ou-ZepE), of course."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trigram_model_filepath = os.path.join(intermediate_directory,\n",
" 'trigram_model_all')"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 4.01 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# this is a bit time consuming - make the if statement True\n",
"# if you want to execute modeling yourself.\n",
"if 1 == 1:\n",
"\n",
" trigram_model = Phrases(bigram_sentences)\n",
"\n",
" trigram_model.save(trigram_model_filepath)\n",
" \n",
"# load the finished model from disk\n",
"trigram_model = Phrases.load(trigram_model_filepath)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll apply our trained second-order phrase model to our first-order transformed sentences, write the results out to a new file, and explore a few of the second-order transformed sentences."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trigram_sentences_filepath = os.path.join(intermediate_directory,\n",
" 'trigram_sentences_all.txt')"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\aashis_tiwari\\AppData\\Local\\Continuum\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\gensim\\models\\phrases.py:274: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
" warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 6.12 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# this is a bit time consuming - make the if statement True\n",
"# if you want to execute data prep yourself.\n",
"if 1 == 1:\n",
"\n",
" with codecs.open(trigram_sentences_filepath, 'w', encoding='utf_8') as f:\n",
" \n",
" for bigram_sentence in bigram_sentences:\n",
" \n",
" trigram_sentence = ' '.join(trigram_model[bigram_sentence])\n",
" \n",
" f.write(trigram_sentence + '\\n')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trigram_sentences = LineSentence(trigram_sentences_filepath)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"new_orleans ap a man_who_allegedly plow_into a crowd enjoy the krewe of endymion parade on_saturday in the mid city section of new_orleans be be investigate for drive while intoxicated police_say\n",
"\n"
]
}
],
"source": [
"for trigram_sentence in it.islice(trigram_sentences, 24, 25):\n",
" print(' '.join(trigram_sentence))\n",
" print('')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks like the second-order phrase model was successful. We're now seeing three-word phrases, such as \"`vanilla_ice_cream`\" and \"`cinnamon_ice_cream`\"."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The final step of our text preparation process circles back to the complete text of the reviews. We're going to run the complete text of the reviews through a pipeline that applies our text normalization and phrase models.\n",
"\n",
"In addition, we'll remove stopwords at this point. _Stopwords_ are very common words, like _a_, _the_, _and_, and so on, that serve functional roles in natural language, but typically don't contribute to the overall meaning of text. Filtering stopwords is a common procedure that allows higher-level NLP modeling techniques to focus on the words that carry more semantic weight.\n",
"\n",
"Finally, we'll write the transformed text out to a new file, with one review per line."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trigram_reviews_filepath = os.path.join(intermediate_directory,\n",
" 'trigram_transformed_reviews_all.txt')"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\aashis_tiwari\\AppData\\Local\\Continuum\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\gensim\\models\\phrases.py:274: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
" warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 52.7 s\n"
]
}
],
"source": [
" %%time\n",
"# %debug\n",
"\n",
"# this is a bit time consuming - make the if statement True\n",
"# if you want to execute data prep yourself.\n",
"if 1 == 1:\n",
"\n",
" with codecs.open(trigram_reviews_filepath, 'w', encoding='utf_8') as f:\n",
" \n",
" for parsed_desc in nlp.pipe(line_review(desc_txt_filepath),\n",
" batch_size=10000, n_threads=4):\n",
" \n",
" # lemmatize the text, removing punctuation and whitespace\n",
" unigram_review = [token.lemma_ for token in parsed_desc\n",
" if not punct_space(token)]\n",
" \n",
" # apply the first-order and second-order phrase models\n",
" bigram_review = bigram_model[unigram_review]\n",
" trigram_review = trigram_model[bigram_review]\n",
" \n",
" # remove any remaining stopwords\n",
" trigram_review = [term for term in trigram_review\n",
" if term not in spacy.en.stop_words.STOP_WORDS]\n",
" \n",
" \n",
" # write the transformed review as a line in the new file\n",
" trigram_review = ' '.join(trigram_review)\n",
" f.write(trigram_review + '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's preview the results. We'll grab one review from the file with the original, untransformed text, grab the same review from the file with the normalized and transformed text, and compare the two."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original:\n",
"\n",
"\"The American coach of Olympic champion Mo Farah may have broken anti-doping rules to boost the performance of some of his athletes, says a leaked report.\"\r\n",
"\n",
"----\n",
"\n",
"Transformed:\n",
"\n",
"american coach olympic_champion mo_farah break anti_doping rule boost performance athlete leak report\n",
"\n"
]
}
],
"source": [
"print('Original:' + '\\n')\n",
"\n",
"for desc in it.islice(line_review(desc_txt_filepath), 23, 24):\n",
" print(desc)\n",
"\n",
"print('----' + '\\n')\n",
"print('Transformed:' + '\\n')\n",
"\n",
"with codecs.open(trigram_reviews_filepath, encoding='utf_8') as f:\n",
" for desc in it.islice(f, 23, 24):\n",
" print(desc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can see that most of the grammatical structure has been scrubbed from the text &mdash; capitalization, articles/conjunctions, punctuation, spacing, etc. However, much of the general semantic *meaning* is still present. Also, multi-word concepts such as \"`friday_night`\" and \"`above_average`\" have been joined into single tokens, as expected. The review text is now ready for higher-level modeling. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Topic Modeling with Latent Dirichlet Allocation (_LDA_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Topic modeling* is family of techniques that can be used to describe and summarize the documents in a corpus according to a set of latent \"topics\". For this demo, we'll be using [*Latent Dirichlet Allocation*](http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf) or LDA, a popular approach to topic modeling.\n",
"\n",
"In many conventional NLP applications, documents are represented a mixture of the individual tokens (words and phrases) they contain. In other words, a document is represented as a *vector* of token counts. There are two layers in this model &mdash; documents and tokens &mdash; and the size or dimensionality of the document vectors is the number of tokens in the corpus vocabulary. This approach has a number of disadvantages:\n",
"* Document vectors tend to be large (one dimension for each token $\\Rightarrow$ lots of dimensions)\n",
"* They also tend to be very sparse. Any given document only contains a small fraction of all tokens in the vocabulary, so most values in the document's token vector are 0.\n",
"* The dimensions are fully indepedent from each other &mdash; there's no sense of connection between related tokens, such as _knife_ and _fork_.\n",
"\n",
"LDA injects a third layer into this conceptual model. Documents are represented as a mixture of a pre-defined number of *topics*, and the *topics* are represented as a mixture of the individual tokens in the vocabulary. The number of topics is a model hyperparameter selected by the practitioner. LDA makes a prior assumption that the (document, topic) and (topic, token) mixtures follow [*Dirichlet*](https://en.wikipedia.org/wiki/Dirichlet_distribution) probability distributions. This assumption encourages documents to consist mostly of a handful of topics, and topics to consist mostly of a modest set of the tokens."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![LDA](https://s3.amazonaws.com/skipgram-images/LDA.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"LDA is fully unsupervised. The topics are \"discovered\" automatically from the data by trying to maximize the likelihood of observing the documents in your corpus, given the modeling assumptions. They are expected to capture some latent structure and organization within the documents, and often have a meaningful human interpretation for people familiar with the subject material.\n",
"\n",
"We'll again turn to gensim to assist with data preparation and modeling. In particular, gensim offers a high-performance parallelized implementation of LDA with its [**LdaMulticore**](https://radimrehurek.com/gensim/models/ldamulticore.html) class."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from gensim.corpora import Dictionary, MmCorpus\n",
"from gensim.models.ldamulticore import LdaMulticore\n",
"\n",
"import pyLDAvis\n",
"import pyLDAvis.gensim\n",
"import warnings\n",
"import _pickle as pickle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first step to creating an LDA model is to learn the full vocabulary of the corpus to be modeled. We'll use gensim's [**Dictionary**](https://radimrehurek.com/gensim/corpora/dictionary.html) class for this."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trigram_dictionary_filepath = os.path.join(intermediate_directory,\n",
" 'trigram_dict_all.dict')"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 1.94 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# this is a bit time consuming - make the if statement True\n",
"# if you want to learn the dictionary yourself.\n",
"if 1 == 1:\n",
"\n",
" trigram_reviews = LineSentence(trigram_reviews_filepath)\n",
"\n",
" # learn the dictionary by iterating over all of the reviews\n",
" trigram_dictionary = Dictionary(trigram_reviews)\n",
" \n",
" # filter tokens that are very rare or too common from\n",
" # the dictionary (filter_extremes) and reassign integer ids (compactify)\n",
" trigram_dictionary.filter_extremes(no_below=10, no_above=0.4)\n",
" trigram_dictionary.compactify()\n",
"\n",
" trigram_dictionary.save(trigram_dictionary_filepath)\n",
" \n",
"# load the finished dictionary from disk\n",
"trigram_dictionary = Dictionary.load(trigram_dictionary_filepath)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Like many NLP techniques, LDA uses a simplifying assumption known as the [*bag-of-words* model](https://en.wikipedia.org/wiki/Bag-of-words_model). In the bag-of-words model, a document is represented by the counts of distinct terms that occur within it. Additional information, such as word order, is discarded. \n",
"\n",
"Using the gensim Dictionary we learned to generate a bag-of-words representation for each review. The `trigram_bow_generator` function implements this. We'll save the resulting bag-of-words reviews as a matrix.\n",
"\n",
"In the following code, \"bag-of-words\" is abbreviated as `bow`."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trigram_bow_filepath = os.path.join(intermediate_directory,\n",
" 'trigram_bow_corpus_all.mm')"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def trigram_bow_generator(filepath):\n",
" \"\"\"\n",
" generator function to read reviews from a file\n",
" and yield a bag-of-words representation\n",
" \"\"\"\n",
" \n",
" for reason in LineSentence(filepath):\n",
" yield trigram_dictionary.doc2bow(reason)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 3.18 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# this is a bit time consuming - make the if statement True\n",
"# if you want to build the bag-of-words corpus yourself.\n",
"if 1 == 1:\n",
"\n",
" # generate bag-of-words representations for\n",
" # all reviews and save them as a matrix\n",
" MmCorpus.serialize(trigram_bow_filepath,\n",
" trigram_bow_generator(trigram_reviews_filepath))\n",
" \n",
"# load the finished bag-of-words corpus from disk\n",
"trigram_bow_corpus = MmCorpus(trigram_bow_filepath)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the bag-of-words corpus, we're finally ready to learn our topic model from the reviews. We simply need to pass the bag-of-words matrix and Dictionary from our previous steps to `LdaMulticore` as inputs, along with the number of topics the model should learn. For this demo, we're asking for 50 topics."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lda_model_filepath = os.path.join(intermediate_directory, 'lda_model_all')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 1min 6s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# this is a bit time consuming - make the if statement True\n",
"# if you want to train the LDA model yourself.\n",
"if 1 == 1:\n",
"\n",
" with warnings.catch_warnings():\n",
" warnings.simplefilter('ignore')\n",
" \n",
" # workers => sets the parallelism, and should be\n",
" # set to your number of physical cores minus one\n",
" lda = LdaMulticore(trigram_bow_corpus,\n",
" num_topics=20,\n",
" id2word=trigram_dictionary,\n",
" workers=1)\n",
" \n",
" lda.save(lda_model_filepath)\n",
" \n",
"# load the finished LDA model from disk\n",
"lda = LdaMulticore.load(lda_model_filepath)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our topic model is now trained and ready to use! Since each topic is represented as a mixture of tokens, you can manually inspect which tokens have been grouped together into which topics to try to understand the patterns the model has discovered in the data."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def explore_topic(topic_number, topn=20):\n",
" \"\"\"\n",
" accept a user-supplied topic number and\n",
" print out a formatted list of the top terms\n",
" \"\"\"\n",
" \n",
" print('{:20} {}'.format(u'term', u'frequency') + '\\n')\n",
"\n",
" for term, frequency in lda.show_topic(topic_number, topn=20):\n",
" print('{:20} {:.3f}'.format(term, round(frequency, 3)))"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"term frequency\n",
"\n",
"'s 0.030\n",
"world 0.007\n",
"time 0.006\n",
"turn 0.006\n",
"sevilla 0.006\n",
"on_wednesday 0.006\n",
"big 0.006\n",
"decision 0.006\n",
"house_intelligence_committee 0.005\n",
"northeast 0.005\n",
"asia 0.005\n",
"work 0.005\n",
"force 0.005\n",
"champion 0.005\n",
"say_he 0.005\n",
"short 0.005\n",
"economy 0.005\n",
"blame 0.005\n",
"hack 0.005\n",
"use 0.004\n"
]
}
],
"source": [
"explore_topic(topic_number=19)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first topic has strong associations with words like *taco*, *salsa*, *chip*, *burrito*, and *margarita*, as well as a handful of more general words. You might call this the **Mexican food** topic!"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"topic_names = {\n",
" 0: 'Soccer - EPL',\n",
" 1: 'US Elections',\n",
" 2: 'US Budget',\n",
" 3: 'US Politics',\n",
" 4: 'Travel Ban',\n",
" 5: 'Online Stores',\n",
" 6: 'Executive Order',\n",
" 7: 'UNK1',\n",
" 8: 'UNK2',\n",
" 9: 'sports',\n",
" 10: 'Europe',\n",
" 11: 'Crime',\n",
" 12: 'UNK3',\n",
" 13: 'UNK4',\n",
" 14: 'Entertainment',\n",
" 15: 'TV series',\n",
" 16: 'social',\n",
" 17: 'World Politics',\n",
" 18: 'UNK5',\n",
" 19: 'UNK6' \n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"topic_names_filepath = os.path.join(intermediate_directory, 'topic_names.pkl')\n",
"\n",
"with open(topic_names_filepath, 'wb') as f:\n",
" pickle.dump(topic_names, f)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"LDAvis_data_filepath = os.path.join(intermediate_directory, 'ldavis_prepared')"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 1min 6s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# this is a bit time consuming - make the if statement True\n",
"# if you want to execute data prep yourself.\n",
"if 1 == 1:\n",
"\n",
" LDAvis_prepared = pyLDAvis.gensim.prepare(lda, trigram_bow_corpus,\n",
" trigram_dictionary)\n",
"\n",
" with open(LDAvis_data_filepath, 'wb') as f:\n",
" pickle.dump(LDAvis_prepared, f)\n",
" \n",
"# load the pre-prepared pyLDAvis data from disk\n",
"with open(LDAvis_data_filepath, \"rb\") as f:\n",
" LDAvis_prepared = pickle.load(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`pyLDAvis.display(...)` displays the topic model visualization in-line in the notebook."
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
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"\n",
"<div id=\"ldavis_el768025710221694568148448306\"></div>\n",
"<script type=\"text/javascript\">\n",
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Describing text with LDA\n",
"Beyond data exploration, one of the key uses for an LDA model is providing a compact, quantitative description of natural language text. Once an LDA model has been trained, it can be used to represent free text as a mixture of the topics the model learned from the original corpus. This mixture can be interpreted as a probability distribution across the topics, so the LDA representation of a paragraph of text might look like 50% _Topic A_, 20% _Topic B_, 20% _Topic C_, and 10% _Topic D_.\n",
"\n",
"To use an LDA model to generate a vector representation of new text, you'll need to apply any text preprocessing steps you used on the model's training corpus to the new text, too. For our model, the preprocessing steps we used include:\n",
"1. Using spaCy to remove punctuation and lemmatize the text\n",
"1. Applying our first-order phrase model to join word pairs\n",
"1. Applying our second-order phrase model to join longer phrases\n",
"1. Removing stopwords\n",
"1. Creating a bag-of-words representation\n",
"\n",
"Once you've applied these preprocessing steps to the new text, it's ready to pass directly to the model to create an LDA representation. The `lda_description(...)` function will perform all these steps for us, including printing the resulting topical description of the input text."
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_sample_desc(desc):\n",
" \"\"\"\n",
" retrieve a particular review index\n",
" from the reviews file and return it\n",
" \"\"\"\n",
" \n",
" return list(it.islice(line_review(desc_txt_filepath),\n",
" desc, desc+1))[0]"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def lda_description(desc_text, min_topic_freq=0.05):\n",
" \"\"\"\n",
" accept the original text of a review and (1) parse it with spaCy,\n",
" (2) apply text pre-proccessing steps, (3) create a bag-of-words\n",
" representation, (4) create an LDA representation, and\n",
" (5) print a sorted list of the top topics in the LDA representation\n",
" \"\"\"\n",
" \n",
" # parse the review text with spaCy\n",
" parsed_desc = nlp(desc_text)\n",
" \n",
" # lemmatize the text and remove punctuation and whitespace\n",
" unigram_desc = [token.lemma_ for token in parsed_desc\n",
" if not punct_space(token)]\n",
" \n",
" # apply the first-order and secord-order phrase models\n",
" bigram_desc = bigram_model[unigram_desc]\n",
" trigram_desc = trigram_model[bigram_desc]\n",
" \n",
" # remove any remaining stopwords\n",
" trigram_desc = [term for term in trigram_desc\n",
" if not term in spacy.en.stop_words.STOP_WORDS]\n",
" \n",
" # create a bag-of-words representation\n",
" desc_bow = trigram_dictionary.doc2bow(trigram_desc)\n",
" \n",
" # create an LDA representation\n",
" desc_lda = lda[desc_bow]\n",
" \n",
" # sort with the most highly related topics first\n",
" desc_lda = sorted(desc_lda, key=lambda topic_lda: -topic_lda[1])\n",
" \n",
" for topic_number, freq in desc_lda:\n",
" if freq < min_topic_freq:\n",
" break\n",
" \n",
" # print the most highly related topic names and frequencies\n",
" print('{:2} {}'.format(topic_names[topic_number],\n",
" round(freq, 2)))"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\"A UK watchdog says so-called Islamic State plans \"\"indiscriminate attacks on innocent civilians\"\".\"\r\n",
"\n"
]
}
],
"source": [
"sample_desc = get_sample_desc(25)\n",
"print(sample_desc)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Entertainment 0.51\n",
"Crime 0.21\n",
"Travel Ban 0.16\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\aashis_tiwari\\AppData\\Local\\Continuum\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\gensim\\models\\phrases.py:274: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
" warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
]
}
],
"source": [
"lda_description(sample_desc)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\"Former Coronation Street actress Paula Williamson, 36, spoke about her seven-year career in the sex industry to Bronson during a visit to HMP Wakefield.\"\r\n",
"\n"
]
}
],
"source": [
"sample_desc= get_sample_desc(100)\n",
"print(sample_desc)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"US Budget 0.37\n",
"UNK4 0.25\n",
"TV series 0.18\n",
"UNK3 0.11\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\aashis_tiwari\\AppData\\Local\\Continuum\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\gensim\\models\\phrases.py:274: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
" warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
]
}
],
"source": [
"lda_description(sample_desc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Word Vector Embedding with Word2Vec"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pop quiz! Can you complete this text snippet?\n",
"\n",
"<br><br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![word2vec quiz](https://s3.amazonaws.com/skipgram-images/word2vec-1.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br><br><br>\n",
"You just demonstrated the core machine learning concept behind word vector embedding models!\n",
"<br><br><br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![word2vec quiz 2](https://s3.amazonaws.com/skipgram-images/word2vec-2.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The goal of *word vector embedding models*, or *word vector models* for short, is to learn dense, numerical vector representations for each term in a corpus vocabulary. If the model is successful, the vectors it learns about each term should encode some information about the *meaning* or *concept* the term represents, and the relationship between it and other terms in the vocabulary. Word vector models are also fully unsupervised &mdash they learn all of these meanings and relationships solely by analyzing the text of the corpus, without any advance knowledge provided.\n",
"\n",
"Perhaps the best-known word vector model is [word2vec](https://arxiv.org/pdf/1301.3781v3.pdf), originally proposed in 2013. The general idea of word2vec is, for a given *focus word*, to use the *context* of the word &mdash; i.e., the other words immediately before and after it &mdash; to provide hints about what the focus word might mean. To do this, word2vec uses a *sliding window* technique, where it considers snippets of text only a few tokens long at a time.\n",
"\n",
"At the start of the learning process, the model initializes random vectors for all terms in the corpus vocabulary. The model then slides the window across every snippet of text in the corpus, with each word taking turns as the focus word. Each time the model considers a new snippet, it tries to learn some information about the focus word based on the surrouding context, and it \"nudges\" the words' vector representations accordingly. One complete pass sliding the window across all of the corpus text is known as a training *epoch*. It's common to train a word2vec model for multiple passes/epochs over the corpus. Over time, the model rearranges the terms' vector representations such that terms that frequently appear in similar contexts have vector representations that are *close* to each other in vector space.\n",
"\n",
"For a deeper dive into word2vec's machine learning process, see [here](https://arxiv.org/pdf/1411.2738v4.pdf).\n",
"\n",
"Word2vec has a number of user-defined hyperparameters, including:\n",
"- The dimensionality of the vectors. Typical choices include a few dozen to several hundred.\n",
"- The width of the sliding window, in tokens. Five is a common default choice, but narrower and wider windows are possible.\n",
"- The number of training epochs.\n",
"\n",
"For using word2vec in Python, [gensim](https://rare-technologies.com/deep-learning-with-word2vec-and-gensim/) comes to the rescue again! It offers a [highly-optimized](https://rare-technologies.com/word2vec-in-python-part-two-optimizing/), [parallelized](https://rare-technologies.com/parallelizing-word2vec-in-python/) implementation of the word2vec algorithm with its [Word2Vec](https://radimrehurek.com/gensim/models/word2vec.html) class."
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from gensim.models import Word2Vec\n",
"\n",
"trigram_sentences = LineSentence(trigram_sentences_filepath)\n",
"word2vec_filepath = os.path.join(intermediate_directory, 'word2vec_model_all')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll train our word2vec model using the normalized sentences with our phrase models applied. We'll use 100-dimensional vectors, and set up our training process to run for twelve epochs."
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"12 training epochs so far.\n",
"Wall time: 3min 29s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# this is a bit time consuming - make the if statement True\n",
"# if you want to train the word2vec model yourself.\n",
"if 1 == 1:\n",
"\n",
" # initiate the model and perform the first epoch of training\n",
" recall2vec = Word2Vec(trigram_sentences, size=100, window=5,\n",
" min_count=20, sg=1, workers=1)\n",
" \n",
" recall2vec.save(word2vec_filepath)\n",
"\n",
" # perform another 11 epochs of training\n",
" for i in range(1,12):\n",
"\n",
" recall2vec.train(trigram_sentences)\n",
" recall2vec.save(word2vec_filepath)\n",
" \n",
"# load the finished model from disk\n",
"recall2vec = Word2Vec.load(word2vec_filepath)\n",
"recall2vec.init_sims()\n",
"\n",
"print('{} training epochs so far.'.format(recall2vec.train_count))"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3,574 terms in the recall2vec vocabulary.\n"
]
}
],
"source": [
"print('{:,} terms in the recall2vec vocabulary.'.format(len(recall2vec.wv.vocab)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a peek at the word vectors our model has learned. We'll create a pandas DataFrame with the terms as the row labels, and the 100 dimensions of the word vector model as the columns."
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
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" <td>0.087219</td>\n",
" <td>...</td>\n",
" <td>-0.199871</td>\n",
" <td>-0.231434</td>\n",
" <td>-0.076976</td>\n",
" <td>0.149460</td>\n",
" <td>-0.022972</td>\n",
" <td>-0.125215</td>\n",
" <td>0.128036</td>\n",
" <td>0.174413</td>\n",
" <td>0.088293</td>\n",
" <td>-0.063436</td>\n",
" </tr>\n",
" <tr>\n",
" <th>to</th>\n",
" <td>-0.074343</td>\n",
" <td>0.023017</td>\n",
" <td>0.080571</td>\n",
" <td>0.234742</td>\n",
" <td>0.036571</td>\n",
" <td>-0.074866</td>\n",
" <td>-0.095007</td>\n",
" <td>0.001376</td>\n",
" <td>-0.018024</td>\n",
" <td>0.207994</td>\n",
" <td>...</td>\n",
" <td>-0.191816</td>\n",
" <td>-0.027370</td>\n",
" <td>0.034580</td>\n",
" <td>-0.003843</td>\n",
" <td>-0.249868</td>\n",
" <td>-0.157277</td>\n",
" <td>0.223993</td>\n",
" <td>0.033954</td>\n",
" <td>0.169415</td>\n",
" <td>-0.245618</td>\n",
" </tr>\n",
" <tr>\n",
" <th>of</th>\n",
" <td>0.029145</td>\n",
" <td>0.105133</td>\n",
" <td>0.138611</td>\n",
" <td>-0.015117</td>\n",
" <td>-0.082952</td>\n",
" <td>-0.043018</td>\n",
" <td>-0.057190</td>\n",
" <td>0.133605</td>\n",
" <td>0.013662</td>\n",
" <td>-0.029294</td>\n",
" <td>...</td>\n",
" <td>-0.153332</td>\n",
" <td>-0.123974</td>\n",
" <td>0.109207</td>\n",
" <td>0.017210</td>\n",
" <td>0.092598</td>\n",
" <td>0.000070</td>\n",
" <td>0.040338</td>\n",
" <td>-0.110079</td>\n",
" <td>0.351764</td>\n",
" <td>-0.242360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>in</th>\n",
" <td>0.082453</td>\n",
" <td>0.014389</td>\n",
" <td>0.113877</td>\n",
" <td>0.200570</td>\n",
" <td>-0.021273</td>\n",
" <td>-0.104283</td>\n",
" <td>-0.158816</td>\n",
" <td>0.223708</td>\n",
" <td>0.124502</td>\n",
" <td>-0.078993</td>\n",
" <td>...</td>\n",
" <td>-0.154863</td>\n",
" <td>-0.156308</td>\n",
" <td>-0.082026</td>\n",
" <td>-0.000164</td>\n",
" <td>-0.042860</td>\n",
" <td>-0.214295</td>\n",
" <td>0.300133</td>\n",
" <td>-0.075646</td>\n",
" <td>0.136757</td>\n",
" <td>0.020359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>and</th>\n",
" <td>0.123636</td>\n",
" <td>-0.019682</td>\n",
" <td>0.003538</td>\n",
" <td>0.083421</td>\n",
" <td>0.139577</td>\n",
" <td>-0.012999</td>\n",
" <td>-0.114522</td>\n",
" <td>0.128361</td>\n",
" <td>-0.057063</td>\n",
" <td>-0.096928</td>\n",
" <td>...</td>\n",
" <td>-0.130418</td>\n",
" <td>-0.228353</td>\n",
" <td>0.051937</td>\n",
" <td>-0.094229</td>\n",
" <td>-0.036228</td>\n",
" <td>-0.058150</td>\n",
" <td>0.202557</td>\n",
" <td>-0.038099</td>\n",
" <td>0.248225</td>\n",
" <td>-0.318656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>have</th>\n",
" <td>-0.028097</td>\n",
" <td>-0.062568</td>\n",
" <td>0.158553</td>\n",
" <td>0.223041</td>\n",
" <td>-0.063416</td>\n",
" <td>0.090147</td>\n",
" <td>-0.140434</td>\n",
" <td>-0.127836</td>\n",
" <td>-0.066042</td>\n",
" <td>0.055509</td>\n",
" <td>...</td>\n",
" <td>-0.207106</td>\n",
" <td>-0.418309</td>\n",
" <td>-0.066265</td>\n",
" <td>-0.014496</td>\n",
" <td>-0.090792</td>\n",
" <td>0.014138</td>\n",
" <td>0.120813</td>\n",
" <td>-0.016787</td>\n",
" <td>0.286776</td>\n",
" <td>-0.064353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>for</th>\n",
" <td>-0.017072</td>\n",
" <td>-0.013024</td>\n",
" <td>0.094448</td>\n",
" <td>0.220435</td>\n",
" <td>0.138748</td>\n",
" <td>0.002925</td>\n",
" <td>-0.191999</td>\n",
" <td>-0.112060</td>\n",
" <td>0.058797</td>\n",
" <td>-0.070895</td>\n",
" <td>...</td>\n",
" <td>-0.355704</td>\n",
" <td>-0.272579</td>\n",
" <td>-0.247733</td>\n",
" <td>0.096593</td>\n",
" <td>-0.039814</td>\n",
" <td>-0.175989</td>\n",
" <td>0.241105</td>\n",
" <td>-0.023037</td>\n",
" <td>0.163303</td>\n",
" <td>-0.083945</td>\n",
" </tr>\n",
" <tr>\n",
" <th>on</th>\n",
" <td>-0.040842</td>\n",
" <td>0.136549</td>\n",
" <td>0.212088</td>\n",
" <td>0.160631</td>\n",
" <td>0.218874</td>\n",
" <td>0.080837</td>\n",
" <td>-0.124079</td>\n",
" <td>0.023630</td>\n",
" <td>0.019694</td>\n",
" <td>-0.104102</td>\n",
" <td>...</td>\n",
" <td>-0.109263</td>\n",
" <td>-0.078680</td>\n",
" <td>-0.118618</td>\n",
" <td>-0.062546</td>\n",
" <td>-0.272106</td>\n",
" <td>-0.074692</td>\n",
" <td>0.196852</td>\n",
" <td>-0.097493</td>\n",
" <td>0.118408</td>\n",
" <td>-0.159755</td>\n",
" </tr>\n",
" <tr>\n",
" <th>that</th>\n",
" <td>0.093854</td>\n",
" <td>0.048086</td>\n",
" <td>0.189431</td>\n",
" <td>0.150064</td>\n",
" <td>0.201977</td>\n",
" <td>-0.129287</td>\n",
" <td>-0.113823</td>\n",
" <td>-0.035287</td>\n",
" <td>-0.024984</td>\n",
" <td>0.184889</td>\n",
" <td>...</td>\n",
" <td>-0.382150</td>\n",
" <td>-0.345845</td>\n",
" <td>0.201678</td>\n",
" <td>-0.030638</td>\n",
" <td>-0.071151</td>\n",
" <td>-0.068005</td>\n",
" <td>-0.081576</td>\n",
" <td>-0.001712</td>\n",
" <td>0.108104</td>\n",
" <td>-0.065577</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'s</th>\n",
" <td>0.107918</td>\n",
" <td>-0.099459</td>\n",
" <td>0.062600</td>\n",
" <td>0.280599</td>\n",
" <td>-0.160545</td>\n",
" <td>-0.148823</td>\n",
" <td>-0.434433</td>\n",
" <td>0.109987</td>\n",
" <td>-0.161235</td>\n",
" <td>-0.090311</td>\n",
" <td>...</td>\n",
" <td>-0.206353</td>\n",
" <td>-0.080761</td>\n",
" <td>0.029325</td>\n",
" <td>-0.183005</td>\n",
" <td>0.239528</td>\n",
" <td>-0.194297</td>\n",
" <td>0.020890</td>\n",
" <td>-0.084427</td>\n",
" <td>0.049696</td>\n",
" <td>0.263197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>with</th>\n",
" <td>0.089093</td>\n",
" <td>-0.118851</td>\n",
" <td>0.213242</td>\n",
" <td>0.171211</td>\n",
" <td>0.021800</td>\n",
" <td>0.052413</td>\n",
" <td>-0.128442</td>\n",
" <td>0.122767</td>\n",
" <td>0.155626</td>\n",
" <td>0.061677</td>\n",
" <td>...</td>\n",
" <td>-0.248516</td>\n",
" <td>-0.212480</td>\n",
" <td>-0.080773</td>\n",
" <td>-0.139916</td>\n",
" <td>-0.198563</td>\n",
" <td>-0.048316</td>\n",
" <td>0.340835</td>\n",
" <td>0.040646</td>\n",
" <td>0.261817</td>\n",
" <td>-0.172303</td>\n",
" </tr>\n",
" <tr>\n",
" <th>as</th>\n",
" <td>0.133016</td>\n",
" <td>0.121538</td>\n",
" <td>0.013165</td>\n",
" <td>0.166905</td>\n",
" <td>0.041944</td>\n",
" <td>-0.204601</td>\n",
" <td>-0.252500</td>\n",
" <td>-0.114080</td>\n",
" <td>0.014661</td>\n",
" <td>-0.205491</td>\n",
" <td>...</td>\n",
" <td>-0.044368</td>\n",
" <td>-0.406310</td>\n",
" <td>0.077290</td>\n",
" <td>-0.117506</td>\n",
" <td>-0.068170</td>\n",
" <td>-0.354395</td>\n",
" <td>0.179773</td>\n",
" <td>0.079178</td>\n",
" <td>0.162803</td>\n",
" <td>-0.297174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>his</th>\n",
" <td>0.171508</td>\n",
" <td>-0.290043</td>\n",
" <td>0.047867</td>\n",
" <td>0.210284</td>\n",
" <td>-0.155969</td>\n",
" <td>-0.216889</td>\n",
" <td>-0.224248</td>\n",
" <td>-0.156078</td>\n",
" <td>0.065083</td>\n",
" <td>-0.049467</td>\n",
" <td>...</td>\n",
" <td>0.113901</td>\n",
" <td>-0.386885</td>\n",
" <td>-0.039495</td>\n",
" <td>-0.136518</td>\n",
" <td>-0.224259</td>\n",
" <td>0.100869</td>\n",
" <td>-0.115878</td>\n",
" <td>-0.000225</td>\n",
" <td>0.332125</td>\n",
" <td>-0.111637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>say</th>\n",
" <td>0.493930</td>\n",
" <td>-0.095527</td>\n",
" <td>0.093631</td>\n",
" <td>0.201500</td>\n",
" <td>0.020013</td>\n",
" <td>0.116634</td>\n",
" <td>-0.480928</td>\n",
" <td>0.015777</td>\n",
" <td>-0.019351</td>\n",
" <td>-0.060809</td>\n",
" <td>...</td>\n",
" <td>-0.406270</td>\n",
" <td>-0.325448</td>\n",
" <td>-0.084951</td>\n",
" <td>-0.056228</td>\n",
" <td>0.207731</td>\n",
" <td>-0.123777</td>\n",
" <td>0.180303</td>\n",
" <td>0.251365</td>\n",
" <td>0.002213</td>\n",
" <td>-0.080268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>’s</th>\n",
" <td>0.130829</td>\n",
" <td>-0.153473</td>\n",
" <td>0.135104</td>\n",
" <td>0.017754</td>\n",
" <td>-0.276074</td>\n",
" <td>-0.299559</td>\n",
" <td>-0.412439</td>\n",
" <td>0.087559</td>\n",
" <td>-0.172711</td>\n",
" <td>-0.124084</td>\n",
" <td>...</td>\n",
" <td>-0.282549</td>\n",
" <td>-0.116469</td>\n",
" <td>0.150934</td>\n",
" <td>-0.113941</td>\n",
" <td>0.254529</td>\n",
" <td>-0.150956</td>\n",
" <td>-0.007050</td>\n",
" <td>-0.046397</td>\n",
" <td>0.246985</td>\n",
" <td>0.100586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>from</th>\n",
" <td>0.103806</td>\n",
" <td>0.199351</td>\n",
" <td>0.091876</td>\n",
" <td>0.157300</td>\n",
" <td>-0.009988</td>\n",
" <td>-0.102036</td>\n",
" <td>-0.154254</td>\n",
" <td>0.211758</td>\n",
" <td>-0.143986</td>\n",
" <td>-0.028054</td>\n",
" <td>...</td>\n",
" <td>-0.380072</td>\n",
" <td>-0.084805</td>\n",
" <td>-0.003880</td>\n",
" <td>-0.052789</td>\n",
" <td>-0.195883</td>\n",
" <td>0.008878</td>\n",
" <td>0.346471</td>\n",
" <td>0.033958</td>\n",
" <td>0.218801</td>\n",
" <td>-0.250586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>it</th>\n",
" <td>-0.054202</td>\n",
" <td>-0.032125</td>\n",
" <td>0.198204</td>\n",
" <td>0.250412</td>\n",
" <td>0.024657</td>\n",
" <td>-0.186574</td>\n",
" <td>0.112915</td>\n",
" <td>-0.295744</td>\n",
" <td>-0.139313</td>\n",
" <td>0.251547</td>\n",
" <td>...</td>\n",
" <td>-0.279481</td>\n",
" <td>-0.156260</td>\n",
" <td>0.041205</td>\n",
" <td>-0.178788</td>\n",
" <td>-0.041812</td>\n",
" <td>-0.115585</td>\n",
" <td>0.026354</td>\n",
" <td>-0.061815</td>\n",
" <td>0.290082</td>\n",
" <td>-0.032485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>at</th>\n",
" <td>0.079581</td>\n",
" <td>-0.000693</td>\n",
" <td>0.018640</td>\n",
" <td>0.242077</td>\n",
" <td>0.086511</td>\n",
" <td>0.104776</td>\n",
" <td>-0.264976</td>\n",
" <td>0.343815</td>\n",
" <td>0.189448</td>\n",
" <td>-0.222301</td>\n",
" <td>...</td>\n",
" <td>-0.184719</td>\n",
" <td>-0.042091</td>\n",
" <td>-0.178990</td>\n",
" <td>0.091708</td>\n",
" <td>-0.253203</td>\n",
" <td>-0.055851</td>\n",
" <td>0.155620</td>\n",
" <td>0.083591</td>\n",
" <td>0.149747</td>\n",
" <td>0.083020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>will</th>\n",
" <td>-0.042439</td>\n",
" <td>-0.020270</td>\n",
" <td>0.201362</td>\n",
" <td>0.076684</td>\n",
" <td>0.260038</td>\n",
" <td>-0.066543</td>\n",
" <td>-0.191507</td>\n",
" <td>0.144495</td>\n",
" <td>-0.255550</td>\n",
" <td>0.070531</td>\n",
" <td>...</td>\n",
" <td>-0.232788</td>\n",
" <td>-0.348006</td>\n",
" <td>-0.207393</td>\n",
" <td>-0.122815</td>\n",
" <td>0.107602</td>\n",
" <td>-0.291866</td>\n",
" <td>0.068789</td>\n",
" <td>-0.266164</td>\n",
" <td>0.141740</td>\n",
" <td>-0.026873</td>\n",
" </tr>\n",
" <tr>\n",
" <th>by</th>\n",
" <td>0.019896</td>\n",
" <td>-0.030638</td>\n",
" <td>0.243529</td>\n",
" <td>0.038790</td>\n",
" <td>-0.104134</td>\n",
" <td>0.029319</td>\n",
" <td>-0.174362</td>\n",
" <td>-0.143550</td>\n",
" <td>-0.082092</td>\n",
" <td>-0.052250</td>\n",
" <td>...</td>\n",
" <td>-0.407827</td>\n",
" <td>-0.404954</td>\n",
" <td>0.021610</td>\n",
" <td>0.131534</td>\n",
" <td>-0.212842</td>\n",
" <td>-0.027826</td>\n",
" <td>0.210552</td>\n",
" <td>0.021041</td>\n",
" <td>0.064949</td>\n",
" <td>-0.098548</td>\n",
" </tr>\n",
" <tr>\n",
" <th>but</th>\n",
" <td>-0.051860</td>\n",
" <td>0.073157</td>\n",
" <td>-0.099655</td>\n",
" <td>0.341471</td>\n",
" <td>0.409402</td>\n",
" <td>-0.102062</td>\n",
" <td>-0.066381</td>\n",
" <td>-0.158641</td>\n",
" <td>-0.071911</td>\n",
" <td>0.131325</td>\n",
" <td>...</td>\n",
" <td>-0.314212</td>\n",
" <td>-0.253961</td>\n",
" <td>-0.236251</td>\n",
" <td>-0.016752</td>\n",
" <td>0.125307</td>\n",
" <td>-0.181046</td>\n",
" <td>0.145657</td>\n",
" <td>0.077196</td>\n",
" <td>0.255184</td>\n",
" <td>0.091203</td>\n",
" </tr>\n",
" <tr>\n",
" <th>after</th>\n",
" <td>-0.280454</td>\n",
" <td>-0.092870</td>\n",
" <td>0.193905</td>\n",
" <td>0.113299</td>\n",
" <td>-0.209316</td>\n",
" <td>-0.172729</td>\n",
" <td>-0.426950</td>\n",
" <td>0.028789</td>\n",
" <td>0.167250</td>\n",
" <td>-0.184772</td>\n",
" <td>...</td>\n",
" <td>-0.276633</td>\n",
" <td>-0.256954</td>\n",
" <td>0.016954</td>\n",
" <td>-0.041827</td>\n",
" <td>-0.026944</td>\n",
" <td>0.051736</td>\n",
" <td>0.325163</td>\n",
" <td>-0.049565</td>\n",
" <td>0.106467</td>\n",
" <td>-0.021257</td>\n",
" </tr>\n",
" <tr>\n",
" <th>he</th>\n",
" <td>-0.252683</td>\n",
" <td>-0.152927</td>\n",
" <td>0.174465</td>\n",
" <td>0.095605</td>\n",
" <td>-0.000625</td>\n",
" <td>-0.176990</td>\n",
" <td>0.091624</td>\n",
" <td>-0.197264</td>\n",
" <td>0.027711</td>\n",
" <td>0.212121</td>\n",
" <td>...</td>\n",
" <td>-0.008932</td>\n",
" <td>-0.614262</td>\n",
" <td>0.028741</td>\n",
" <td>0.034528</td>\n",
" <td>-0.187046</td>\n",
" <td>-0.093244</td>\n",
" <td>0.056804</td>\n",
" <td>-0.056108</td>\n",
" <td>0.381933</td>\n",
" <td>0.039332</td>\n",
" </tr>\n",
" <tr>\n",
" <th>an</th>\n",
" <td>-0.005633</td>\n",
" <td>-0.174258</td>\n",
" <td>0.170194</td>\n",
" <td>0.324523</td>\n",
" <td>0.190754</td>\n",
" <td>0.159505</td>\n",
" <td>0.127698</td>\n",
" <td>0.307520</td>\n",
" <td>-0.027873</td>\n",
" <td>-0.035499</td>\n",
" <td>...</td>\n",
" <td>-0.052373</td>\n",
" <td>-0.405566</td>\n",
" <td>0.001079</td>\n",
" <td>0.196531</td>\n",
" <td>0.033124</td>\n",
" <td>0.091576</td>\n",
" <td>0.149526</td>\n",
" <td>0.278049</td>\n",
" <td>0.180986</td>\n",
" <td>-0.062273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>their</th>\n",
" <td>0.122311</td>\n",
" <td>-0.164332</td>\n",
" <td>-0.035862</td>\n",
" <td>0.147095</td>\n",
" <td>0.021767</td>\n",
" <td>-0.100748</td>\n",
" <td>-0.170448</td>\n",
" <td>0.171713</td>\n",
" <td>-0.105358</td>\n",
" <td>-0.015916</td>\n",
" <td>...</td>\n",
" <td>0.151549</td>\n",
" <td>-0.126537</td>\n",
" <td>-0.016259</td>\n",
" <td>-0.084716</td>\n",
" <td>-0.083166</td>\n",
" <td>-0.054485</td>\n",
" <td>0.069355</td>\n",
" <td>0.014472</td>\n",
" <td>0.015852</td>\n",
" <td>-0.424737</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new</th>\n",
" <td>0.153179</td>\n",
" <td>0.181721</td>\n",
" <td>0.033603</td>\n",
" <td>-0.165886</td>\n",
" <td>0.016452</td>\n",
" <td>0.073054</td>\n",
" <td>0.088210</td>\n",
" <td>0.013292</td>\n",
" <td>0.030326</td>\n",
" <td>0.393300</td>\n",
" <td>...</td>\n",
" <td>-0.122008</td>\n",
" <td>-0.201035</td>\n",
" <td>0.085460</td>\n",
" <td>0.167302</td>\n",
" <td>-0.171616</td>\n",
" <td>0.072928</td>\n",
" <td>-0.210929</td>\n",
" <td>-0.031279</td>\n",
" <td>0.036854</td>\n",
" <td>-0.311985</td>\n",
" </tr>\n",
" <tr>\n",
" <th>not</th>\n",
" <td>-0.054297</td>\n",
" <td>0.020709</td>\n",
" <td>0.233996</td>\n",
" <td>0.225498</td>\n",
" <td>-0.110124</td>\n",
" <td>0.232670</td>\n",
" <td>0.093244</td>\n",
" <td>-0.412237</td>\n",
" <td>-0.215938</td>\n",
" <td>0.074204</td>\n",
" <td>...</td>\n",
" <td>-0.609410</td>\n",
" <td>-0.422543</td>\n",
" <td>-0.104661</td>\n",
" <td>-0.021803</td>\n",
" <td>0.103312</td>\n",
" <td>0.252440</td>\n",
" <td>0.035054</td>\n",
" <td>-0.171981</td>\n",
" <td>0.129241</td>\n",
" <td>0.091124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>its</th>\n",
" <td>0.284080</td>\n",
" <td>0.123173</td>\n",
" <td>0.278765</td>\n",
" <td>0.098248</td>\n",
" <td>-0.150397</td>\n",
" <td>-0.243362</td>\n",
" <td>-0.248603</td>\n",
" <td>0.066457</td>\n",
" <td>-0.131231</td>\n",
" <td>-0.010582</td>\n",
" <td>...</td>\n",
" <td>-0.075695</td>\n",
" <td>-0.138076</td>\n",
" <td>0.035898</td>\n",
" <td>-0.324406</td>\n",
" <td>0.155565</td>\n",
" <td>0.171583</td>\n",
" <td>0.192598</td>\n",
" <td>-0.114135</td>\n",
" <td>0.060844</td>\n",
" <td>0.022733</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>not_enough</th>\n",
" <td>-0.094114</td>\n",
" <td>0.655539</td>\n",
" <td>0.626017</td>\n",
" <td>-0.217886</td>\n",
" <td>0.167621</td>\n",
" <td>-0.072231</td>\n",
" <td>0.309021</td>\n",
" <td>-0.250300</td>\n",
" <td>0.606297</td>\n",
" <td>0.567899</td>\n",
" <td>...</td>\n",
" <td>-1.623047</td>\n",
" <td>-0.510145</td>\n",
" <td>0.267852</td>\n",
" <td>-0.092845</td>\n",
" <td>-0.470826</td>\n",
" <td>-0.076695</td>\n",
" <td>-0.124101</td>\n",
" <td>-0.398636</td>\n",
" <td>-0.561430</td>\n",
" <td>0.478688</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ranking</th>\n",
" <td>0.201723</td>\n",
" <td>-0.029949</td>\n",
" <td>0.289470</td>\n",
" <td>-0.733708</td>\n",
" <td>-0.170238</td>\n",
" <td>1.398432</td>\n",
" <td>-0.349454</td>\n",
" <td>0.714751</td>\n",
" <td>0.140537</td>\n",
" <td>0.285269</td>\n",
" <td>...</td>\n",
" <td>-0.330462</td>\n",
" <td>-0.259038</td>\n",
" <td>0.025241</td>\n",
" <td>0.164333</td>\n",
" <td>-0.237639</td>\n",
" <td>-0.039643</td>\n",
" <td>0.115805</td>\n",
" <td>-0.261110</td>\n",
" <td>-0.064060</td>\n",
" <td>0.887719</td>\n",
" </tr>\n",
" <tr>\n",
" <th>antonio</th>\n",
" <td>-0.030697</td>\n",
" <td>-1.538885</td>\n",
" <td>0.484067</td>\n",
" <td>0.909993</td>\n",
" <td>0.407148</td>\n",
" <td>0.424210</td>\n",
" <td>0.146013</td>\n",
" <td>-0.730521</td>\n",
" <td>-0.060478</td>\n",
" <td>0.085379</td>\n",
" <td>...</td>\n",
" <td>-0.659557</td>\n",
" <td>-0.091135</td>\n",
" <td>-0.286684</td>\n",
" <td>-0.350696</td>\n",
" <td>0.157299</td>\n",
" <td>-1.042287</td>\n",
" <td>0.082264</td>\n",
" <td>0.011264</td>\n",
" <td>0.102585</td>\n",
" <td>0.571744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sight</th>\n",
" <td>-1.224476</td>\n",
" <td>0.187305</td>\n",
" <td>0.203670</td>\n",
" <td>0.580851</td>\n",
" <td>-0.644513</td>\n",
" <td>-0.268522</td>\n",
" <td>-0.144188</td>\n",
" <td>0.297203</td>\n",
" <td>0.025232</td>\n",
" <td>0.954260</td>\n",
" <td>...</td>\n",
" <td>-0.150982</td>\n",
" <td>-0.302174</td>\n",
" <td>0.505027</td>\n",
" <td>-0.005202</td>\n",
" <td>0.272650</td>\n",
" <td>-0.635197</td>\n",
" <td>-0.547206</td>\n",
" <td>0.037410</td>\n",
" <td>0.074470</td>\n",
" <td>-0.178576</td>\n",
" </tr>\n",
" <tr>\n",
" <th>advantage</th>\n",
" <td>0.622663</td>\n",
" <td>0.296375</td>\n",
" <td>0.850668</td>\n",
" <td>-0.228654</td>\n",
" <td>0.418743</td>\n",
" <td>-0.174389</td>\n",
" <td>0.262017</td>\n",
" <td>0.058859</td>\n",
" <td>0.379051</td>\n",
" <td>0.102179</td>\n",
" <td>...</td>\n",
" <td>-0.707169</td>\n",
" <td>-0.221860</td>\n",
" <td>0.282483</td>\n",
" <td>-0.726319</td>\n",
" <td>-0.781960</td>\n",
" <td>-0.128981</td>\n",
" <td>0.388335</td>\n",
" <td>0.194783</td>\n",
" <td>-0.324332</td>\n",
" <td>-0.569256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>world_champion</th>\n",
" <td>-0.285938</td>\n",
" <td>0.124571</td>\n",
" <td>0.176119</td>\n",
" <td>1.228446</td>\n",
" <td>0.026598</td>\n",
" <td>0.410035</td>\n",
" <td>-0.064291</td>\n",
" <td>0.206982</td>\n",
" <td>-0.231103</td>\n",
" <td>-0.156042</td>\n",
" <td>...</td>\n",
" <td>-0.332884</td>\n",
" <td>-0.530765</td>\n",
" <td>0.222084</td>\n",
" <td>-0.142474</td>\n",
" <td>-0.651072</td>\n",
" <td>-0.155838</td>\n",
" <td>-0.514741</td>\n",
" <td>0.075556</td>\n",
" <td>-0.029474</td>\n",
" <td>0.679655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pipeline</th>\n",
" <td>0.744847</td>\n",
" <td>1.070185</td>\n",
" <td>-0.039550</td>\n",
" <td>0.570992</td>\n",
" <td>0.570726</td>\n",
" <td>-0.719932</td>\n",
" <td>-0.344005</td>\n",
" <td>-0.125998</td>\n",
" <td>-0.077002</td>\n",
" <td>0.206826</td>\n",
" <td>...</td>\n",
" <td>-0.338752</td>\n",
" <td>0.389430</td>\n",
" <td>-0.053337</td>\n",
" <td>0.551466</td>\n",
" <td>0.434493</td>\n",
" <td>-0.113832</td>\n",
" <td>-0.230845</td>\n",
" <td>0.228762</td>\n",
" <td>-0.729973</td>\n",
" <td>-0.094345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>commerce</th>\n",
" <td>0.799601</td>\n",
" <td>-0.485471</td>\n",
" <td>-0.237045</td>\n",
" <td>-0.010754</td>\n",
" <td>0.047780</td>\n",
" <td>0.104614</td>\n",
" <td>-0.332199</td>\n",
" <td>0.473448</td>\n",
" <td>0.004946</td>\n",
" <td>0.683338</td>\n",
" <td>...</td>\n",
" <td>-0.873561</td>\n",
" <td>-0.819303</td>\n",
" <td>0.663663</td>\n",
" <td>0.199147</td>\n",
" <td>-0.248849</td>\n",
" <td>-0.134445</td>\n",
" <td>0.100175</td>\n",
" <td>-0.408534</td>\n",
" <td>0.197245</td>\n",
" <td>1.066751</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hello</th>\n",
" <td>0.049729</td>\n",
" <td>-0.111662</td>\n",
" <td>0.030879</td>\n",
" <td>0.231366</td>\n",
" <td>1.303890</td>\n",
" <td>-0.240264</td>\n",
" <td>0.135847</td>\n",
" <td>-0.237629</td>\n",
" <td>-0.640210</td>\n",
" <td>0.976841</td>\n",
" <td>...</td>\n",
" <td>-0.068632</td>\n",
" <td>-0.545131</td>\n",
" <td>0.403947</td>\n",
" <td>0.188938</td>\n",
" <td>0.327679</td>\n",
" <td>0.362776</td>\n",
" <td>-0.805201</td>\n",
" <td>0.101312</td>\n",
" <td>0.428273</td>\n",
" <td>-0.541816</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ciro_immobile</th>\n",
" <td>0.078922</td>\n",
" <td>0.030445</td>\n",
" <td>0.677406</td>\n",
" <td>1.054021</td>\n",
" <td>-0.438968</td>\n",
" <td>-0.997223</td>\n",
" <td>0.584563</td>\n",
" <td>-0.522375</td>\n",
" <td>0.171615</td>\n",
" <td>0.665406</td>\n",
" <td>...</td>\n",
" <td>0.500395</td>\n",
" <td>-1.398964</td>\n",
" <td>0.397761</td>\n",
" <td>0.300816</td>\n",
" <td>0.072580</td>\n",
" <td>0.198801</td>\n",
" <td>0.108731</td>\n",
" <td>-0.045659</td>\n",
" <td>0.654901</td>\n",
" <td>-0.462221</td>\n",
" </tr>\n",
" <tr>\n",
" <th>trademark</th>\n",
" <td>0.499815</td>\n",
" <td>0.139723</td>\n",
" <td>-0.122960</td>\n",
" <td>-0.396945</td>\n",
" <td>0.074634</td>\n",
" <td>-0.253119</td>\n",
" <td>-0.114092</td>\n",
" <td>-0.216992</td>\n",
" <td>0.738331</td>\n",
" <td>0.201665</td>\n",
" <td>...</td>\n",
" <td>-0.722312</td>\n",
" <td>-0.237891</td>\n",
" <td>0.188983</td>\n",
" <td>-0.134771</td>\n",
" <td>0.592018</td>\n",
" <td>0.599179</td>\n",
" <td>0.785352</td>\n",
" <td>0.247860</td>\n",
" <td>-0.705473</td>\n",
" <td>-0.257353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>refer_to</th>\n",
" <td>-0.062879</td>\n",
" <td>0.077788</td>\n",
" <td>-0.374180</td>\n",
" <td>0.468723</td>\n",
" <td>0.353635</td>\n",
" <td>0.131391</td>\n",
" <td>0.633991</td>\n",
" <td>0.109901</td>\n",
" <td>-0.282035</td>\n",
" <td>0.264723</td>\n",
" <td>...</td>\n",
" <td>-0.721140</td>\n",
" <td>-0.962518</td>\n",
" <td>-0.101024</td>\n",
" <td>0.428971</td>\n",
" <td>-0.780233</td>\n",
" <td>-0.247224</td>\n",
" <td>-0.250521</td>\n",
" <td>0.017568</td>\n",
" <td>0.353447</td>\n",
" <td>-0.612467</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sign_up</th>\n",
" <td>-0.252607</td>\n",
" <td>-0.721285</td>\n",
" <td>-0.062100</td>\n",
" <td>0.437720</td>\n",
" <td>0.980251</td>\n",
" <td>-0.016084</td>\n",
" <td>-0.149467</td>\n",
" <td>0.760577</td>\n",
" <td>0.664475</td>\n",
" <td>0.186012</td>\n",
" <td>...</td>\n",
" <td>-0.933546</td>\n",
" <td>-0.592306</td>\n",
" <td>0.077836</td>\n",
" <td>0.694950</td>\n",
" <td>-0.125671</td>\n",
" <td>-0.063421</td>\n",
" <td>0.060527</td>\n",
" <td>-0.170805</td>\n",
" <td>-0.192611</td>\n",
" <td>0.015800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>candy</th>\n",
" <td>0.171377</td>\n",
" <td>0.952507</td>\n",
" <td>0.485432</td>\n",
" <td>0.059742</td>\n",
" <td>-0.103659</td>\n",
" <td>-0.541383</td>\n",
" <td>0.331817</td>\n",
" <td>0.439239</td>\n",
" <td>-0.517162</td>\n",
" <td>0.518952</td>\n",
" <td>...</td>\n",
" <td>-0.783613</td>\n",
" <td>0.272791</td>\n",
" <td>0.576765</td>\n",
" <td>0.115468</td>\n",
" <td>-0.322823</td>\n",
" <td>0.524259</td>\n",
" <td>-0.479514</td>\n",
" <td>-0.336677</td>\n",
" <td>0.357923</td>\n",
" <td>-0.509631</td>\n",
" </tr>\n",
" <tr>\n",
" <th>miami</th>\n",
" <td>0.431808</td>\n",
" <td>0.485360</td>\n",
" <td>-0.258010</td>\n",
" <td>-0.030971</td>\n",
" <td>0.839913</td>\n",
" <td>0.439697</td>\n",
" <td>-0.278369</td>\n",
" <td>-0.373473</td>\n",
" <td>0.230637</td>\n",
" <td>0.312374</td>\n",
" <td>...</td>\n",
" <td>-0.011426</td>\n",
" <td>-0.862144</td>\n",
" <td>-1.416061</td>\n",
" <td>-0.243965</td>\n",
" <td>0.828267</td>\n",
" <td>-0.180654</td>\n",
" <td>-0.382431</td>\n",
" <td>-0.093430</td>\n",
" <td>-0.152998</td>\n",
" <td>-0.178208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>physical</th>\n",
" <td>-0.598984</td>\n",
" <td>0.181450</td>\n",
" <td>0.074859</td>\n",
" <td>0.204118</td>\n",
" <td>-0.006442</td>\n",
" <td>0.395756</td>\n",
" <td>0.570412</td>\n",
" <td>0.205016</td>\n",
" <td>-0.061954</td>\n",
" <td>0.414210</td>\n",
" <td>...</td>\n",
" <td>-0.148652</td>\n",
" <td>-0.780257</td>\n",
" <td>0.972387</td>\n",
" <td>0.527897</td>\n",
" <td>-0.543720</td>\n",
" <td>0.092487</td>\n",
" <td>0.383912</td>\n",
" <td>-0.118773</td>\n",
" <td>-0.293702</td>\n",
" <td>-0.146384</td>\n",
" </tr>\n",
" <tr>\n",
" <th>recover</th>\n",
" <td>0.730891</td>\n",
" <td>0.883145</td>\n",
" <td>-0.160776</td>\n",
" <td>1.012670</td>\n",
" <td>0.640895</td>\n",
" <td>0.124774</td>\n",
" <td>-0.033443</td>\n",
" <td>-0.090705</td>\n",
" <td>0.564360</td>\n",
" <td>-0.220390</td>\n",
" <td>...</td>\n",
" <td>-0.390584</td>\n",
" <td>-0.560045</td>\n",
" <td>0.072854</td>\n",
" <td>0.432087</td>\n",
" <td>0.117072</td>\n",
" <td>-0.579653</td>\n",
" <td>0.061056</td>\n",
" <td>-0.139276</td>\n",
" <td>0.751752</td>\n",
" <td>0.225760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sustain</th>\n",
" <td>-0.221599</td>\n",
" <td>-0.629878</td>\n",
" <td>-0.173603</td>\n",
" <td>1.353064</td>\n",
" <td>-0.214283</td>\n",
" <td>-0.208931</td>\n",
" <td>-0.334680</td>\n",
" <td>0.673992</td>\n",
" <td>-0.728771</td>\n",
" <td>-0.604576</td>\n",
" <td>...</td>\n",
" <td>0.269625</td>\n",
" <td>-0.254749</td>\n",
" <td>-0.076043</td>\n",
" <td>-0.295920</td>\n",
" <td>0.247204</td>\n",
" <td>-0.156045</td>\n",
" <td>-0.054930</td>\n",
" <td>-0.128302</td>\n",
" <td>-0.225513</td>\n",
" <td>0.259037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>catholic</th>\n",
" <td>-0.828640</td>\n",
" <td>0.114157</td>\n",
" <td>-0.252447</td>\n",
" <td>0.470119</td>\n",
" <td>-0.066177</td>\n",
" <td>0.724520</td>\n",
" <td>-0.735754</td>\n",
" <td>0.026392</td>\n",
" <td>-0.518175</td>\n",
" <td>0.308917</td>\n",
" <td>...</td>\n",
" <td>0.437098</td>\n",
" <td>-0.487706</td>\n",
" <td>0.084492</td>\n",
" <td>0.454513</td>\n",
" <td>0.574732</td>\n",
" <td>-0.347128</td>\n",
" <td>-0.320074</td>\n",
" <td>-0.063053</td>\n",
" <td>0.497562</td>\n",
" <td>0.301398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>haven't</th>\n",
" <td>0.507012</td>\n",
" <td>0.010656</td>\n",
" <td>-0.293064</td>\n",
" <td>0.072675</td>\n",
" <td>0.145872</td>\n",
" <td>0.713825</td>\n",
" <td>-0.252026</td>\n",
" <td>0.019099</td>\n",
" <td>-0.364322</td>\n",
" <td>-0.184802</td>\n",
" <td>...</td>\n",
" <td>-0.161201</td>\n",
" <td>-0.599765</td>\n",
" <td>-0.249576</td>\n",
" <td>0.608555</td>\n",
" <td>-0.422503</td>\n",
" <td>-0.494418</td>\n",
" <td>0.109678</td>\n",
" <td>-0.752551</td>\n",
" <td>0.096510</td>\n",
" <td>0.191669</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100,000</th>\n",
" <td>0.361202</td>\n",
" <td>-0.753392</td>\n",
" <td>0.987674</td>\n",
" <td>0.240236</td>\n",
" <td>-0.025776</td>\n",
" <td>-0.460963</td>\n",
" <td>-0.215952</td>\n",
" <td>-0.847847</td>\n",
" <td>0.273376</td>\n",
" <td>0.420312</td>\n",
" <td>...</td>\n",
" <td>-0.619123</td>\n",
" <td>0.190498</td>\n",
" <td>0.018086</td>\n",
" <td>0.558629</td>\n",
" <td>0.094480</td>\n",
" <td>-0.478777</td>\n",
" <td>0.142151</td>\n",
" <td>-0.755201</td>\n",
" <td>-0.123881</td>\n",
" <td>0.042677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>upgrade</th>\n",
" <td>-0.012784</td>\n",
" <td>-0.138323</td>\n",
" <td>-0.451248</td>\n",
" <td>0.682032</td>\n",
" <td>0.498691</td>\n",
" <td>0.185636</td>\n",
" <td>0.930943</td>\n",
" <td>0.320989</td>\n",
" <td>0.307036</td>\n",
" <td>1.008475</td>\n",
" <td>...</td>\n",
" <td>-0.028922</td>\n",
" <td>-0.118396</td>\n",
" <td>-0.311720</td>\n",
" <td>0.532235</td>\n",
" <td>-0.178842</td>\n",
" <td>-0.743159</td>\n",
" <td>-0.301638</td>\n",
" <td>0.266679</td>\n",
" <td>-0.604421</td>\n",
" <td>-0.661528</td>\n",
" </tr>\n",
" <tr>\n",
" <th>organizer</th>\n",
" <td>0.336194</td>\n",
" <td>0.882553</td>\n",
" <td>-0.121645</td>\n",
" <td>0.277824</td>\n",
" <td>0.397778</td>\n",
" <td>-0.411582</td>\n",
" <td>-0.574646</td>\n",
" <td>0.409801</td>\n",
" <td>0.119394</td>\n",
" <td>0.048563</td>\n",
" <td>...</td>\n",
" <td>-0.954704</td>\n",
" <td>0.255724</td>\n",
" <td>-0.660715</td>\n",
" <td>-0.701783</td>\n",
" <td>0.726125</td>\n",
" <td>-0.365561</td>\n",
" <td>-0.770863</td>\n",
" <td>0.607819</td>\n",
" <td>0.382359</td>\n",
" <td>0.145916</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cause_by</th>\n",
" <td>-0.021795</td>\n",
" <td>0.969837</td>\n",
" <td>0.861347</td>\n",
" <td>0.724383</td>\n",
" <td>0.549279</td>\n",
" <td>0.285342</td>\n",
" <td>-0.244288</td>\n",
" <td>0.443533</td>\n",
" <td>-0.150323</td>\n",
" <td>-0.044506</td>\n",
" <td>...</td>\n",
" <td>0.055570</td>\n",
" <td>-0.238164</td>\n",
" <td>1.029564</td>\n",
" <td>-0.509440</td>\n",
" <td>0.245553</td>\n",
" <td>0.390072</td>\n",
" <td>0.136188</td>\n",
" <td>-0.759734</td>\n",
" <td>-0.296547</td>\n",
" <td>0.645483</td>\n",
" </tr>\n",
" <tr>\n",
" <th>we_know</th>\n",
" <td>-0.499787</td>\n",
" <td>0.213263</td>\n",
" <td>-0.333230</td>\n",
" <td>-0.215939</td>\n",
" <td>0.425414</td>\n",
" <td>-0.005458</td>\n",
" <td>0.641097</td>\n",
" <td>0.486520</td>\n",
" <td>-0.827362</td>\n",
" <td>0.272523</td>\n",
" <td>...</td>\n",
" <td>-0.876714</td>\n",
" <td>-0.162277</td>\n",
" <td>0.748110</td>\n",
" <td>-0.004144</td>\n",
" <td>1.271405</td>\n",
" <td>-0.213628</td>\n",
" <td>-0.570346</td>\n",
" <td>-0.133997</td>\n",
" <td>-0.092709</td>\n",
" <td>0.171419</td>\n",
" </tr>\n",
" <tr>\n",
" <th>robin</th>\n",
" <td>0.634702</td>\n",
" <td>-0.401851</td>\n",
" <td>0.602035</td>\n",
" <td>0.632417</td>\n",
" <td>-0.611379</td>\n",
" <td>0.719042</td>\n",
" <td>-0.960486</td>\n",
" <td>-0.927092</td>\n",
" <td>0.614439</td>\n",
" <td>0.191374</td>\n",
" <td>...</td>\n",
" <td>1.139387</td>\n",
" <td>-0.230930</td>\n",
" <td>0.102474</td>\n",
" <td>0.884600</td>\n",
" <td>-0.038467</td>\n",
" <td>0.895778</td>\n",
" <td>-0.252214</td>\n",
" <td>-0.616806</td>\n",
" <td>0.670850</td>\n",
" <td>-0.122526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>second_season</th>\n",
" <td>0.225271</td>\n",
" <td>0.363482</td>\n",
" <td>-0.254534</td>\n",
" <td>0.821283</td>\n",
" <td>-0.270632</td>\n",
" <td>0.838620</td>\n",
" <td>0.219087</td>\n",
" <td>-0.154190</td>\n",
" <td>0.112581</td>\n",
" <td>-0.167201</td>\n",
" <td>...</td>\n",
" <td>-0.380116</td>\n",
" <td>-0.505385</td>\n",
" <td>-0.342129</td>\n",
" <td>0.302236</td>\n",
" <td>0.271572</td>\n",
" <td>-0.100411</td>\n",
" <td>-1.278507</td>\n",
" <td>-0.061038</td>\n",
" <td>-0.365958</td>\n",
" <td>0.282088</td>\n",
" </tr>\n",
" <tr>\n",
" <th>psa_group</th>\n",
" <td>0.790442</td>\n",
" <td>-0.037463</td>\n",
" <td>-0.028965</td>\n",
" <td>0.502319</td>\n",
" <td>-0.216150</td>\n",
" <td>-0.210333</td>\n",
" <td>-0.501655</td>\n",
" <td>1.124272</td>\n",
" <td>0.198881</td>\n",
" <td>0.043538</td>\n",
" <td>...</td>\n",
" <td>-0.453107</td>\n",
" <td>-0.672462</td>\n",
" <td>-0.601512</td>\n",
" <td>-0.051346</td>\n",
" <td>-0.381387</td>\n",
" <td>-0.292804</td>\n",
" <td>0.184509</td>\n",
" <td>0.359980</td>\n",
" <td>-0.430942</td>\n",
" <td>-0.408788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>wideout</th>\n",
" <td>0.137189</td>\n",
" <td>0.162664</td>\n",
" <td>-0.302007</td>\n",
" <td>0.751640</td>\n",
" <td>0.292836</td>\n",
" <td>0.073071</td>\n",
" <td>-0.417419</td>\n",
" <td>0.519182</td>\n",
" <td>-0.909194</td>\n",
" <td>0.243708</td>\n",
" <td>...</td>\n",
" <td>-1.043700</td>\n",
" <td>-1.066392</td>\n",
" <td>-0.737779</td>\n",
" <td>0.633879</td>\n",
" <td>-0.501979</td>\n",
" <td>-0.016485</td>\n",
" <td>-0.403879</td>\n",
" <td>-0.129087</td>\n",
" <td>0.032780</td>\n",
" <td>0.369346</td>\n",
" </tr>\n",
" <tr>\n",
" <th>every_week</th>\n",
" <td>0.387804</td>\n",
" <td>-0.128606</td>\n",
" <td>-1.109899</td>\n",
" <td>0.574474</td>\n",
" <td>-0.973595</td>\n",
" <td>0.051985</td>\n",
" <td>-0.443664</td>\n",
" <td>0.547075</td>\n",
" <td>0.484658</td>\n",
" <td>0.809539</td>\n",
" <td>...</td>\n",
" <td>-1.128321</td>\n",
" <td>-0.469462</td>\n",
" <td>0.188889</td>\n",
" <td>0.110277</td>\n",
" <td>-0.764626</td>\n",
" <td>-0.380393</td>\n",
" <td>-0.640461</td>\n",
" <td>0.177771</td>\n",
" <td>0.653821</td>\n",
" <td>-0.147864</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3574 rows × 100 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 \\\n",
"the 0.002429 -0.117696 0.081637 0.150256 -0.095737 -0.037604 \n",
"be -0.038380 -0.065649 0.117450 0.012678 0.115999 -0.044034 \n",
"a 0.039630 -0.006076 0.242709 0.164494 0.018715 -0.068876 \n",
"to -0.074343 0.023017 0.080571 0.234742 0.036571 -0.074866 \n",
"of 0.029145 0.105133 0.138611 -0.015117 -0.082952 -0.043018 \n",
"in 0.082453 0.014389 0.113877 0.200570 -0.021273 -0.104283 \n",
"and 0.123636 -0.019682 0.003538 0.083421 0.139577 -0.012999 \n",
"have -0.028097 -0.062568 0.158553 0.223041 -0.063416 0.090147 \n",
"for -0.017072 -0.013024 0.094448 0.220435 0.138748 0.002925 \n",
"on -0.040842 0.136549 0.212088 0.160631 0.218874 0.080837 \n",
"that 0.093854 0.048086 0.189431 0.150064 0.201977 -0.129287 \n",
"'s 0.107918 -0.099459 0.062600 0.280599 -0.160545 -0.148823 \n",
"with 0.089093 -0.118851 0.213242 0.171211 0.021800 0.052413 \n",
"as 0.133016 0.121538 0.013165 0.166905 0.041944 -0.204601 \n",
"his 0.171508 -0.290043 0.047867 0.210284 -0.155969 -0.216889 \n",
"say 0.493930 -0.095527 0.093631 0.201500 0.020013 0.116634 \n",
"’s 0.130829 -0.153473 0.135104 0.017754 -0.276074 -0.299559 \n",
"from 0.103806 0.199351 0.091876 0.157300 -0.009988 -0.102036 \n",
"it -0.054202 -0.032125 0.198204 0.250412 0.024657 -0.186574 \n",
"at 0.079581 -0.000693 0.018640 0.242077 0.086511 0.104776 \n",
"will -0.042439 -0.020270 0.201362 0.076684 0.260038 -0.066543 \n",
"by 0.019896 -0.030638 0.243529 0.038790 -0.104134 0.029319 \n",
"but -0.051860 0.073157 -0.099655 0.341471 0.409402 -0.102062 \n",
"after -0.280454 -0.092870 0.193905 0.113299 -0.209316 -0.172729 \n",
"he -0.252683 -0.152927 0.174465 0.095605 -0.000625 -0.176990 \n",
"an -0.005633 -0.174258 0.170194 0.324523 0.190754 0.159505 \n",
"their 0.122311 -0.164332 -0.035862 0.147095 0.021767 -0.100748 \n",
"new 0.153179 0.181721 0.033603 -0.165886 0.016452 0.073054 \n",
"not -0.054297 0.020709 0.233996 0.225498 -0.110124 0.232670 \n",
"its 0.284080 0.123173 0.278765 0.098248 -0.150397 -0.243362 \n",
"... ... ... ... ... ... ... \n",
"not_enough -0.094114 0.655539 0.626017 -0.217886 0.167621 -0.072231 \n",
"ranking 0.201723 -0.029949 0.289470 -0.733708 -0.170238 1.398432 \n",
"antonio -0.030697 -1.538885 0.484067 0.909993 0.407148 0.424210 \n",
"sight -1.224476 0.187305 0.203670 0.580851 -0.644513 -0.268522 \n",
"advantage 0.622663 0.296375 0.850668 -0.228654 0.418743 -0.174389 \n",
"world_champion -0.285938 0.124571 0.176119 1.228446 0.026598 0.410035 \n",
"pipeline 0.744847 1.070185 -0.039550 0.570992 0.570726 -0.719932 \n",
"commerce 0.799601 -0.485471 -0.237045 -0.010754 0.047780 0.104614 \n",
"hello 0.049729 -0.111662 0.030879 0.231366 1.303890 -0.240264 \n",
"ciro_immobile 0.078922 0.030445 0.677406 1.054021 -0.438968 -0.997223 \n",
"trademark 0.499815 0.139723 -0.122960 -0.396945 0.074634 -0.253119 \n",
"refer_to -0.062879 0.077788 -0.374180 0.468723 0.353635 0.131391 \n",
"sign_up -0.252607 -0.721285 -0.062100 0.437720 0.980251 -0.016084 \n",
"candy 0.171377 0.952507 0.485432 0.059742 -0.103659 -0.541383 \n",
"miami 0.431808 0.485360 -0.258010 -0.030971 0.839913 0.439697 \n",
"physical -0.598984 0.181450 0.074859 0.204118 -0.006442 0.395756 \n",
"recover 0.730891 0.883145 -0.160776 1.012670 0.640895 0.124774 \n",
"sustain -0.221599 -0.629878 -0.173603 1.353064 -0.214283 -0.208931 \n",
"catholic -0.828640 0.114157 -0.252447 0.470119 -0.066177 0.724520 \n",
"haven't 0.507012 0.010656 -0.293064 0.072675 0.145872 0.713825 \n",
"100,000 0.361202 -0.753392 0.987674 0.240236 -0.025776 -0.460963 \n",
"upgrade -0.012784 -0.138323 -0.451248 0.682032 0.498691 0.185636 \n",
"organizer 0.336194 0.882553 -0.121645 0.277824 0.397778 -0.411582 \n",
"cause_by -0.021795 0.969837 0.861347 0.724383 0.549279 0.285342 \n",
"we_know -0.499787 0.213263 -0.333230 -0.215939 0.425414 -0.005458 \n",
"robin 0.634702 -0.401851 0.602035 0.632417 -0.611379 0.719042 \n",
"second_season 0.225271 0.363482 -0.254534 0.821283 -0.270632 0.838620 \n",
"psa_group 0.790442 -0.037463 -0.028965 0.502319 -0.216150 -0.210333 \n",
"wideout 0.137189 0.162664 -0.302007 0.751640 0.292836 0.073071 \n",
"every_week 0.387804 -0.128606 -1.109899 0.574474 -0.973595 0.051985 \n",
"\n",
" 6 7 8 9 ... 90 \\\n",
"the -0.194618 0.193092 -0.062545 -0.091891 ... -0.116249 \n",
"be -0.129380 -0.026304 -0.020176 0.016766 ... -0.265466 \n",
"a -0.149067 -0.053870 0.104615 0.087219 ... -0.199871 \n",
"to -0.095007 0.001376 -0.018024 0.207994 ... -0.191816 \n",
"of -0.057190 0.133605 0.013662 -0.029294 ... -0.153332 \n",
"in -0.158816 0.223708 0.124502 -0.078993 ... -0.154863 \n",
"and -0.114522 0.128361 -0.057063 -0.096928 ... -0.130418 \n",
"have -0.140434 -0.127836 -0.066042 0.055509 ... -0.207106 \n",
"for -0.191999 -0.112060 0.058797 -0.070895 ... -0.355704 \n",
"on -0.124079 0.023630 0.019694 -0.104102 ... -0.109263 \n",
"that -0.113823 -0.035287 -0.024984 0.184889 ... -0.382150 \n",
"'s -0.434433 0.109987 -0.161235 -0.090311 ... -0.206353 \n",
"with -0.128442 0.122767 0.155626 0.061677 ... -0.248516 \n",
"as -0.252500 -0.114080 0.014661 -0.205491 ... -0.044368 \n",
"his -0.224248 -0.156078 0.065083 -0.049467 ... 0.113901 \n",
"say -0.480928 0.015777 -0.019351 -0.060809 ... -0.406270 \n",
"’s -0.412439 0.087559 -0.172711 -0.124084 ... -0.282549 \n",
"from -0.154254 0.211758 -0.143986 -0.028054 ... -0.380072 \n",
"it 0.112915 -0.295744 -0.139313 0.251547 ... -0.279481 \n",
"at -0.264976 0.343815 0.189448 -0.222301 ... -0.184719 \n",
"will -0.191507 0.144495 -0.255550 0.070531 ... -0.232788 \n",
"by -0.174362 -0.143550 -0.082092 -0.052250 ... -0.407827 \n",
"but -0.066381 -0.158641 -0.071911 0.131325 ... -0.314212 \n",
"after -0.426950 0.028789 0.167250 -0.184772 ... -0.276633 \n",
"he 0.091624 -0.197264 0.027711 0.212121 ... -0.008932 \n",
"an 0.127698 0.307520 -0.027873 -0.035499 ... -0.052373 \n",
"their -0.170448 0.171713 -0.105358 -0.015916 ... 0.151549 \n",
"new 0.088210 0.013292 0.030326 0.393300 ... -0.122008 \n",
"not 0.093244 -0.412237 -0.215938 0.074204 ... -0.609410 \n",
"its -0.248603 0.066457 -0.131231 -0.010582 ... -0.075695 \n",
"... ... ... ... ... ... ... \n",
"not_enough 0.309021 -0.250300 0.606297 0.567899 ... -1.623047 \n",
"ranking -0.349454 0.714751 0.140537 0.285269 ... -0.330462 \n",
"antonio 0.146013 -0.730521 -0.060478 0.085379 ... -0.659557 \n",
"sight -0.144188 0.297203 0.025232 0.954260 ... -0.150982 \n",
"advantage 0.262017 0.058859 0.379051 0.102179 ... -0.707169 \n",
"world_champion -0.064291 0.206982 -0.231103 -0.156042 ... -0.332884 \n",
"pipeline -0.344005 -0.125998 -0.077002 0.206826 ... -0.338752 \n",
"commerce -0.332199 0.473448 0.004946 0.683338 ... -0.873561 \n",
"hello 0.135847 -0.237629 -0.640210 0.976841 ... -0.068632 \n",
"ciro_immobile 0.584563 -0.522375 0.171615 0.665406 ... 0.500395 \n",
"trademark -0.114092 -0.216992 0.738331 0.201665 ... -0.722312 \n",
"refer_to 0.633991 0.109901 -0.282035 0.264723 ... -0.721140 \n",
"sign_up -0.149467 0.760577 0.664475 0.186012 ... -0.933546 \n",
"candy 0.331817 0.439239 -0.517162 0.518952 ... -0.783613 \n",
"miami -0.278369 -0.373473 0.230637 0.312374 ... -0.011426 \n",
"physical 0.570412 0.205016 -0.061954 0.414210 ... -0.148652 \n",
"recover -0.033443 -0.090705 0.564360 -0.220390 ... -0.390584 \n",
"sustain -0.334680 0.673992 -0.728771 -0.604576 ... 0.269625 \n",
"catholic -0.735754 0.026392 -0.518175 0.308917 ... 0.437098 \n",
"haven't -0.252026 0.019099 -0.364322 -0.184802 ... -0.161201 \n",
"100,000 -0.215952 -0.847847 0.273376 0.420312 ... -0.619123 \n",
"upgrade 0.930943 0.320989 0.307036 1.008475 ... -0.028922 \n",
"organizer -0.574646 0.409801 0.119394 0.048563 ... -0.954704 \n",
"cause_by -0.244288 0.443533 -0.150323 -0.044506 ... 0.055570 \n",
"we_know 0.641097 0.486520 -0.827362 0.272523 ... -0.876714 \n",
"robin -0.960486 -0.927092 0.614439 0.191374 ... 1.139387 \n",
"second_season 0.219087 -0.154190 0.112581 -0.167201 ... -0.380116 \n",
"psa_group -0.501655 1.124272 0.198881 0.043538 ... -0.453107 \n",
"wideout -0.417419 0.519182 -0.909194 0.243708 ... -1.043700 \n",
"every_week -0.443664 0.547075 0.484658 0.809539 ... -1.128321 \n",
"\n",
" 91 92 93 94 95 96 \\\n",
"the -0.061802 -0.029034 -0.070177 0.058084 -0.125219 0.022664 \n",
"be -0.267629 0.014311 0.022319 0.065673 -0.072681 0.056618 \n",
"a -0.231434 -0.076976 0.149460 -0.022972 -0.125215 0.128036 \n",
"to -0.027370 0.034580 -0.003843 -0.249868 -0.157277 0.223993 \n",
"of -0.123974 0.109207 0.017210 0.092598 0.000070 0.040338 \n",
"in -0.156308 -0.082026 -0.000164 -0.042860 -0.214295 0.300133 \n",
"and -0.228353 0.051937 -0.094229 -0.036228 -0.058150 0.202557 \n",
"have -0.418309 -0.066265 -0.014496 -0.090792 0.014138 0.120813 \n",
"for -0.272579 -0.247733 0.096593 -0.039814 -0.175989 0.241105 \n",
"on -0.078680 -0.118618 -0.062546 -0.272106 -0.074692 0.196852 \n",
"that -0.345845 0.201678 -0.030638 -0.071151 -0.068005 -0.081576 \n",
"'s -0.080761 0.029325 -0.183005 0.239528 -0.194297 0.020890 \n",
"with -0.212480 -0.080773 -0.139916 -0.198563 -0.048316 0.340835 \n",
"as -0.406310 0.077290 -0.117506 -0.068170 -0.354395 0.179773 \n",
"his -0.386885 -0.039495 -0.136518 -0.224259 0.100869 -0.115878 \n",
"say -0.325448 -0.084951 -0.056228 0.207731 -0.123777 0.180303 \n",
"’s -0.116469 0.150934 -0.113941 0.254529 -0.150956 -0.007050 \n",
"from -0.084805 -0.003880 -0.052789 -0.195883 0.008878 0.346471 \n",
"it -0.156260 0.041205 -0.178788 -0.041812 -0.115585 0.026354 \n",
"at -0.042091 -0.178990 0.091708 -0.253203 -0.055851 0.155620 \n",
"will -0.348006 -0.207393 -0.122815 0.107602 -0.291866 0.068789 \n",
"by -0.404954 0.021610 0.131534 -0.212842 -0.027826 0.210552 \n",
"but -0.253961 -0.236251 -0.016752 0.125307 -0.181046 0.145657 \n",
"after -0.256954 0.016954 -0.041827 -0.026944 0.051736 0.325163 \n",
"he -0.614262 0.028741 0.034528 -0.187046 -0.093244 0.056804 \n",
"an -0.405566 0.001079 0.196531 0.033124 0.091576 0.149526 \n",
"their -0.126537 -0.016259 -0.084716 -0.083166 -0.054485 0.069355 \n",
"new -0.201035 0.085460 0.167302 -0.171616 0.072928 -0.210929 \n",
"not -0.422543 -0.104661 -0.021803 0.103312 0.252440 0.035054 \n",
"its -0.138076 0.035898 -0.324406 0.155565 0.171583 0.192598 \n",
"... ... ... ... ... ... ... \n",
"not_enough -0.510145 0.267852 -0.092845 -0.470826 -0.076695 -0.124101 \n",
"ranking -0.259038 0.025241 0.164333 -0.237639 -0.039643 0.115805 \n",
"antonio -0.091135 -0.286684 -0.350696 0.157299 -1.042287 0.082264 \n",
"sight -0.302174 0.505027 -0.005202 0.272650 -0.635197 -0.547206 \n",
"advantage -0.221860 0.282483 -0.726319 -0.781960 -0.128981 0.388335 \n",
"world_champion -0.530765 0.222084 -0.142474 -0.651072 -0.155838 -0.514741 \n",
"pipeline 0.389430 -0.053337 0.551466 0.434493 -0.113832 -0.230845 \n",
"commerce -0.819303 0.663663 0.199147 -0.248849 -0.134445 0.100175 \n",
"hello -0.545131 0.403947 0.188938 0.327679 0.362776 -0.805201 \n",
"ciro_immobile -1.398964 0.397761 0.300816 0.072580 0.198801 0.108731 \n",
"trademark -0.237891 0.188983 -0.134771 0.592018 0.599179 0.785352 \n",
"refer_to -0.962518 -0.101024 0.428971 -0.780233 -0.247224 -0.250521 \n",
"sign_up -0.592306 0.077836 0.694950 -0.125671 -0.063421 0.060527 \n",
"candy 0.272791 0.576765 0.115468 -0.322823 0.524259 -0.479514 \n",
"miami -0.862144 -1.416061 -0.243965 0.828267 -0.180654 -0.382431 \n",
"physical -0.780257 0.972387 0.527897 -0.543720 0.092487 0.383912 \n",
"recover -0.560045 0.072854 0.432087 0.117072 -0.579653 0.061056 \n",
"sustain -0.254749 -0.076043 -0.295920 0.247204 -0.156045 -0.054930 \n",
"catholic -0.487706 0.084492 0.454513 0.574732 -0.347128 -0.320074 \n",
"haven't -0.599765 -0.249576 0.608555 -0.422503 -0.494418 0.109678 \n",
"100,000 0.190498 0.018086 0.558629 0.094480 -0.478777 0.142151 \n",
"upgrade -0.118396 -0.311720 0.532235 -0.178842 -0.743159 -0.301638 \n",
"organizer 0.255724 -0.660715 -0.701783 0.726125 -0.365561 -0.770863 \n",
"cause_by -0.238164 1.029564 -0.509440 0.245553 0.390072 0.136188 \n",
"we_know -0.162277 0.748110 -0.004144 1.271405 -0.213628 -0.570346 \n",
"robin -0.230930 0.102474 0.884600 -0.038467 0.895778 -0.252214 \n",
"second_season -0.505385 -0.342129 0.302236 0.271572 -0.100411 -1.278507 \n",
"psa_group -0.672462 -0.601512 -0.051346 -0.381387 -0.292804 0.184509 \n",
"wideout -1.066392 -0.737779 0.633879 -0.501979 -0.016485 -0.403879 \n",
"every_week -0.469462 0.188889 0.110277 -0.764626 -0.380393 -0.640461 \n",
"\n",
" 97 98 99 \n",
"the 0.004382 0.231636 -0.051400 \n",
"be 0.022661 0.291493 0.028192 \n",
"a 0.174413 0.088293 -0.063436 \n",
"to 0.033954 0.169415 -0.245618 \n",
"of -0.110079 0.351764 -0.242360 \n",
"in -0.075646 0.136757 0.020359 \n",
"and -0.038099 0.248225 -0.318656 \n",
"have -0.016787 0.286776 -0.064353 \n",
"for -0.023037 0.163303 -0.083945 \n",
"on -0.097493 0.118408 -0.159755 \n",
"that -0.001712 0.108104 -0.065577 \n",
"'s -0.084427 0.049696 0.263197 \n",
"with 0.040646 0.261817 -0.172303 \n",
"as 0.079178 0.162803 -0.297174 \n",
"his -0.000225 0.332125 -0.111637 \n",
"say 0.251365 0.002213 -0.080268 \n",
"’s -0.046397 0.246985 0.100586 \n",
"from 0.033958 0.218801 -0.250586 \n",
"it -0.061815 0.290082 -0.032485 \n",
"at 0.083591 0.149747 0.083020 \n",
"will -0.266164 0.141740 -0.026873 \n",
"by 0.021041 0.064949 -0.098548 \n",
"but 0.077196 0.255184 0.091203 \n",
"after -0.049565 0.106467 -0.021257 \n",
"he -0.056108 0.381933 0.039332 \n",
"an 0.278049 0.180986 -0.062273 \n",
"their 0.014472 0.015852 -0.424737 \n",
"new -0.031279 0.036854 -0.311985 \n",
"not -0.171981 0.129241 0.091124 \n",
"its -0.114135 0.060844 0.022733 \n",
"... ... ... ... \n",
"not_enough -0.398636 -0.561430 0.478688 \n",
"ranking -0.261110 -0.064060 0.887719 \n",
"antonio 0.011264 0.102585 0.571744 \n",
"sight 0.037410 0.074470 -0.178576 \n",
"advantage 0.194783 -0.324332 -0.569256 \n",
"world_champion 0.075556 -0.029474 0.679655 \n",
"pipeline 0.228762 -0.729973 -0.094345 \n",
"commerce -0.408534 0.197245 1.066751 \n",
"hello 0.101312 0.428273 -0.541816 \n",
"ciro_immobile -0.045659 0.654901 -0.462221 \n",
"trademark 0.247860 -0.705473 -0.257353 \n",
"refer_to 0.017568 0.353447 -0.612467 \n",
"sign_up -0.170805 -0.192611 0.015800 \n",
"candy -0.336677 0.357923 -0.509631 \n",
"miami -0.093430 -0.152998 -0.178208 \n",
"physical -0.118773 -0.293702 -0.146384 \n",
"recover -0.139276 0.751752 0.225760 \n",
"sustain -0.128302 -0.225513 0.259037 \n",
"catholic -0.063053 0.497562 0.301398 \n",
"haven't -0.752551 0.096510 0.191669 \n",
"100,000 -0.755201 -0.123881 0.042677 \n",
"upgrade 0.266679 -0.604421 -0.661528 \n",
"organizer 0.607819 0.382359 0.145916 \n",
"cause_by -0.759734 -0.296547 0.645483 \n",
"we_know -0.133997 -0.092709 0.171419 \n",
"robin -0.616806 0.670850 -0.122526 \n",
"second_season -0.061038 -0.365958 0.282088 \n",
"psa_group 0.359980 -0.430942 -0.408788 \n",
"wideout -0.129087 0.032780 0.369346 \n",
"every_week 0.177771 0.653821 -0.147864 \n",
"\n",
"[3574 rows x 100 columns]"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# build a list of the terms, integer indices,\n",
"# and term counts from the food2vec model vocabulary\n",
"ordered_vocab = [(term, voc.index, voc.count)\n",
" for term, voc in recall2vec.wv.vocab.items()]\n",
"\n",
"# sort by the term counts, so the most common terms appear first\n",
"ordered_vocab = sorted(ordered_vocab, key=lambda term_index: -term_index[2])\n",
"\n",
"# unzip the terms, integer indices, and counts into separate lists\n",
"ordered_terms, term_indices, term_counts = zip(*ordered_vocab)\n",
"\n",
"# create a DataFrame with the food2vec vectors as data,\n",
"# and the terms as row labels\n",
"word_vectors = pd.DataFrame(recall2vec.wv.syn0[term_indices, :],\n",
" index=ordered_terms)\n",
"\n",
"word_vectors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### So... what can we do with all these numbers?\n",
"The first thing we can use them for is to simply look up related words and phrases for a given term of interest."
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_related_terms(token, topn=5):\n",
" \"\"\"\n",
" look up the topn most similar terms to token\n",
" and print them as a formatted list\n",
" \"\"\"\n",
"\n",
" for word, similarity in recall2vec.most_similar(positive=[token], topn=topn):\n",
"\n",
" print('{:20} {}'.format(word, round(similarity, 3)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Its News: So? Donald Trump.."
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"donald_trump 0.649\n",
"president_donald_trump 0.648\n",
"u.s._president_donald 0.646\n",
"president_trump 0.632\n",
"trump_'s 0.632\n"
]
}
],
"source": [
"get_related_terms('trump')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### What happened to Arsenal?"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"bayern_munich 0.73\n",
"manchester_city 0.697\n",
"liverpool 0.689\n",
"manchester_united 0.682\n",
"alexis_sanchez 0.677\n"
]
}
],
"source": [
"get_related_terms('arsenal')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Terms related to travel to US?"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"executive_order 0.471\n",
"refugee 0.421\n",
"new_executive_order 0.397\n",
"bar 0.395\n",
"requirement 0.386\n",
"trip 0.385\n",
"new_travel_ban 0.382\n",
"traveler 0.381\n",
"ban 0.381\n",
"ambassador 0.381\n",
"united_states 0.38\n",
"country 0.377\n",
"boeing 0.364\n",
"entry 0.364\n",
"temporarily 0.364\n",
"visit 0.357\n",
"v 0.355\n",
"restrict 0.353\n",
"citizen 0.353\n",
"arrive 0.352\n"
]
}
],
"source": [
"get_related_terms('travel', topn=20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Word algebra!\n",
"No self-respecting word2vec demo would be complete without a healthy dose of *word algebra*, also known as *analogy completion*.\n",
"\n",
"The core idea is that once words are represented as numerical vectors, you can do math with them. The mathematical procedure goes like this:\n",
"1. Provide a set of words or phrases that you'd like to add or subtract.\n",
"1. Look up the vectors that represent those terms in the word vector model.\n",
"1. Add and subtract those vectors to produce a new, combined vector.\n",
"1. Look up the most similar vector(s) to this new, combined vector via cosine similarity.\n",
"1. Return the word(s) associated with the similar vector(s).\n",
"\n",
"But more generally, you can think of the vectors that represent each word as encoding some information about the *meaning* or *concepts* of the word. What happens when you ask the model to combine the meaning and concepts of words in new ways? Let's see."
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def word_algebra(add=[], subtract=[], topn=1):\n",
" \"\"\"\n",
" combine the vectors associated with the words provided\n",
" in add= and subtract=, look up the topn most similar\n",
" terms to the combined vector, and print the result(s)\n",
" \"\"\"\n",
" answers = recall2vec.most_similar(positive=add, negative=subtract, topn=topn)\n",
" \n",
" for term, similarity in answers:\n",
" print(term)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### League + Sevilla = ? \n",
"Which soccer league does sevilla featured in?"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"champions_league\n"
]
}
],
"source": [
"word_algebra(add=['league', 'sevilla'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK, so the model knows that *Sevilla* featured in *Champions League*. What else?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Channel - Game + Series = ?"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"fx\n"
]
}
],
"source": [
"word_algebra(add=['channel', 'series'], subtract=['game'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we're getting a bit more nuanced. The model has discovered that:\n",
"- Both *lunch* and *dinner* are meals\n",
"- The main difference between them is time of day\n",
"- Day and night are times of day\n",
"- Lunch is associated with day, and dinner is associated with night\n",
"\n",
"What else?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### President - Trump + Obama = ?"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"former_president\n"
]
}
],
"source": [
"word_algebra(add=['president', 'obama'], subtract=['trump'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Word Vector Visualization with t-SNE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[t-Distributed Stochastic Neighbor Embedding](https://lvdmaaten.github.io/publications/papers/JMLR_2008.pdf), or *t-SNE* for short, is a dimensionality reduction technique to assist with visualizing high-dimensional datasets. It attempts to map high-dimensional data onto a low two- or three-dimensional representation such that the relative distances between points are preserved as closely as possible in both high-dimensional and low-dimensional space.\n",
"\n",
"scikit-learn provides a convenient implementation of the t-SNE algorithm with its [TSNE](http://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html) class."
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.manifold import TSNE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our input for t-SNE will be the DataFrame of word vectors we created before. Let's first:\n",
"1. Drop stopwords &mdash; it's probably not too interesting to visualize *the*, *of*, *or*, and so on\n",
"1. Take only the 5,000 most frequent terms in the vocabulary &mdash; no need to visualize all ~50,000 terms right now."
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tsne_input = word_vectors.drop(spacy.en.stop_words.STOP_WORDS, errors='ignore')\n",
"tsne_input = tsne_input.head(1000)"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <td>-0.084427</td>\n",
" <td>0.049696</td>\n",
" <td>0.263197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>’s</th>\n",
" <td>0.130829</td>\n",
" <td>-0.153473</td>\n",
" <td>0.135104</td>\n",
" <td>0.017754</td>\n",
" <td>-0.276074</td>\n",
" <td>-0.299559</td>\n",
" <td>-0.412439</td>\n",
" <td>0.087559</td>\n",
" <td>-0.172711</td>\n",
" <td>-0.124084</td>\n",
" <td>...</td>\n",
" <td>-0.282549</td>\n",
" <td>-0.116469</td>\n",
" <td>0.150934</td>\n",
" <td>-0.113941</td>\n",
" <td>0.254529</td>\n",
" <td>-0.150956</td>\n",
" <td>-0.007050</td>\n",
" <td>-0.046397</td>\n",
" <td>0.246985</td>\n",
" <td>0.100586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new</th>\n",
" <td>0.153179</td>\n",
" <td>0.181721</td>\n",
" <td>0.033603</td>\n",
" <td>-0.165886</td>\n",
" <td>0.016452</td>\n",
" <td>0.073054</td>\n",
" <td>0.088210</td>\n",
" <td>0.013292</td>\n",
" <td>0.030326</td>\n",
" <td>0.393300</td>\n",
" <td>...</td>\n",
" <td>-0.122008</td>\n",
" <td>-0.201035</td>\n",
" <td>0.085460</td>\n",
" <td>0.167302</td>\n",
" <td>-0.171616</td>\n",
" <td>0.072928</td>\n",
" <td>-0.210929</td>\n",
" <td>-0.031279</td>\n",
" <td>0.036854</td>\n",
" <td>-0.311985</td>\n",
" </tr>\n",
" <tr>\n",
" <th>people</th>\n",
" <td>-0.051667</td>\n",
" <td>0.017035</td>\n",
" <td>0.020685</td>\n",
" <td>-0.109309</td>\n",
" <td>0.701161</td>\n",
" <td>0.015426</td>\n",
" <td>-0.047808</td>\n",
" <td>-0.041350</td>\n",
" <td>0.221359</td>\n",
" <td>0.300733</td>\n",
" <td>...</td>\n",
" <td>-0.664428</td>\n",
" <td>-0.472518</td>\n",
" <td>0.516659</td>\n",
" <td>0.041798</td>\n",
" <td>-0.269182</td>\n",
" <td>0.083803</td>\n",
" <td>0.351930</td>\n",
" <td>-0.601797</td>\n",
" <td>0.352331</td>\n",
" <td>-0.094488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>good</th>\n",
" <td>0.066196</td>\n",
" <td>-0.024190</td>\n",
" <td>-0.075152</td>\n",
" <td>0.286012</td>\n",
" <td>0.416342</td>\n",
" <td>0.374844</td>\n",
" <td>0.138737</td>\n",
" <td>-0.079997</td>\n",
" <td>0.251452</td>\n",
" <td>-0.123866</td>\n",
" <td>...</td>\n",
" <td>-0.068765</td>\n",
" <td>-0.313415</td>\n",
" <td>-0.211218</td>\n",
" <td>-0.094767</td>\n",
" <td>-0.085571</td>\n",
" <td>-0.343961</td>\n",
" <td>-0.187723</td>\n",
" <td>-0.341004</td>\n",
" <td>0.163113</td>\n",
" <td>-0.309968</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 100 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 \\\n",
"'s 0.107918 -0.099459 0.062600 0.280599 -0.160545 -0.148823 -0.434433 \n",
"’s 0.130829 -0.153473 0.135104 0.017754 -0.276074 -0.299559 -0.412439 \n",
"new 0.153179 0.181721 0.033603 -0.165886 0.016452 0.073054 0.088210 \n",
"people -0.051667 0.017035 0.020685 -0.109309 0.701161 0.015426 -0.047808 \n",
"good 0.066196 -0.024190 -0.075152 0.286012 0.416342 0.374844 0.138737 \n",
"\n",
" 7 8 9 ... 90 91 92 \\\n",
"'s 0.109987 -0.161235 -0.090311 ... -0.206353 -0.080761 0.029325 \n",
"’s 0.087559 -0.172711 -0.124084 ... -0.282549 -0.116469 0.150934 \n",
"new 0.013292 0.030326 0.393300 ... -0.122008 -0.201035 0.085460 \n",
"people -0.041350 0.221359 0.300733 ... -0.664428 -0.472518 0.516659 \n",
"good -0.079997 0.251452 -0.123866 ... -0.068765 -0.313415 -0.211218 \n",
"\n",
" 93 94 95 96 97 98 99 \n",
"'s -0.183005 0.239528 -0.194297 0.020890 -0.084427 0.049696 0.263197 \n",
"’s -0.113941 0.254529 -0.150956 -0.007050 -0.046397 0.246985 0.100586 \n",
"new 0.167302 -0.171616 0.072928 -0.210929 -0.031279 0.036854 -0.311985 \n",
"people 0.041798 -0.269182 0.083803 0.351930 -0.601797 0.352331 -0.094488 \n",
"good -0.094767 -0.085571 -0.343961 -0.187723 -0.341004 0.163113 -0.309968 \n",
"\n",
"[5 rows x 100 columns]"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tsne_input.head()"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tsne_filepath = os.path.join(intermediate_directory,\n",
" 'tsne_model')\n",
"\n",
"tsne_vectors_filepath = os.path.join(intermediate_directory,\n",
" 'tsne_vectors.npy')"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 13.4 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"if 1 == 1:\n",
" \n",
" tsne = TSNE()\n",
" tsne_vectors = tsne.fit_transform(tsne_input.values)\n",
" \n",
" with open(tsne_filepath, 'wb') as f:\n",
" pickle.dump(tsne, f)\n",
"\n",
" pd.np.save(tsne_vectors_filepath, tsne_vectors)\n",
" \n",
"with open(tsne_filepath, \"rb\") as f:\n",
" tsne = pickle.load(f)\n",
" \n",
"tsne_vectors = pd.np.load(tsne_vectors_filepath)\n",
"\n",
"tsne_vectors = pd.DataFrame(tsne_vectors,\n",
" index=pd.Index(tsne_input.index),\n",
" columns=['x_coord', 'y_coord'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we have a two-dimensional representation of our data! Let's take a look."
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>x_coord</th>\n",
" <th>y_coord</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>'s</th>\n",
" <td>9.415721</td>\n",
" <td>11.288566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>’s</th>\n",
" <td>8.405776</td>\n",
" <td>9.269558</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new</th>\n",
" <td>8.004002</td>\n",
" <td>6.649834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>people</th>\n",
" <td>-2.045634</td>\n",
" <td>8.869362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>good</th>\n",
" <td>5.944308</td>\n",
" <td>3.195368</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" x_coord y_coord\n",
"'s 9.415721 11.288566\n",
"’s 8.405776 9.269558\n",
"new 8.004002 6.649834\n",
"people -2.045634 8.869362\n",
"good 5.944308 3.195368"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tsne_vectors.head()"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tsne_vectors['word'] = tsne_vectors.index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plotting with Bokeh"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\aashis_tiwari\\AppData\\Local\\Continuum\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\bokeh\\core\\json_encoder.py:52: DeprecationWarning: parsing timezone aware datetimes is deprecated; this will raise an error in the future\n",
" NP_EPOCH = np.datetime64('1970-01-01T00:00:00Z')\n"
]
},
{
"data": {
"text/html": [
"\n",
" <div class=\"bk-root\">\n",
" <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
" <span id=\"2d785175-6517-4954-bb04-97394d22f7af\">Loading BokehJS ...</span>\n",
" </div>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"\n",
"(function(global) {\n",
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" return new Date();\n",
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"\n",
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"\n",
" if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
" window._bokeh_onload_callbacks = [];\n",
" window._bokeh_is_loading = undefined;\n",
" }\n",
"\n",
"\n",
" \n",
" if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
" window._bokeh_timeout = Date.now() + 5000;\n",
" window._bokeh_failed_load = false;\n",
" }\n",
"\n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
"\n",
" function display_loaded() {\n",
" if (window.Bokeh !== undefined) {\n",
" document.getElementById(\"2d785175-6517-4954-bb04-97394d22f7af\").textContent = \"BokehJS successfully loaded.\";\n",
" } else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(display_loaded, 100)\n",
" }\n",
" }\n",
"\n",
" function run_callbacks() {\n",
" window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
" delete window._bokeh_onload_callbacks\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
"\n",
" function load_libs(js_urls, callback) {\n",
" window._bokeh_onload_callbacks.push(callback);\n",
" if (window._bokeh_is_loading > 0) {\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" window._bokeh_is_loading = js_urls.length;\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = false;\n",
" s.onreadystatechange = s.onload = function() {\n",
" window._bokeh_is_loading--;\n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
" run_callbacks()\n",
" }\n",
" };\n",
" s.onerror = function() {\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" }\n",
" };var element = document.getElementById(\"2d785175-6517-4954-bb04-97394d22f7af\");\n",
" if (element == null) {\n",
" console.log(\"Bokeh: ERROR: autoload.js configured with elementid '2d785175-6517-4954-bb04-97394d22f7af' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
"\n",
" var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.js\"];\n",
"\n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" Bokeh.set_log_level(\"info\");\n",
" },\n",
" \n",
" function(Bokeh) {\n",
" \n",
" document.getElementById(\"2d785175-6517-4954-bb04-97394d22f7af\").textContent = \"BokehJS is loading...\";\n",
" },\n",
" function(Bokeh) {\n",
" console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
" Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
" console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
" Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
" }\n",
" ];\n",
"\n",
" function run_inline_js() {\n",
" \n",
" if ((window.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!window._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" window._bokeh_failed_load = true;\n",
" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"2d785175-6517-4954-bb04-97394d22f7af\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
"\n",
" }\n",
"\n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
"}(this));"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from bokeh.plotting import figure, show, output_notebook\n",
"from bokeh.models import HoverTool, ColumnDataSource, value\n",
"\n",
"output_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
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" </div>\n",
"<script type=\"text/javascript\">\n",
" \n",
" (function(global) {\n",
" function now() {\n",
" return new Date();\n",
" }\n",
" \n",
" var force = false;\n",
" \n",
" if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
" window._bokeh_onload_callbacks = [];\n",
" window._bokeh_is_loading = undefined;\n",
" }\n",
" \n",
" \n",
" \n",
" if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
" window._bokeh_timeout = Date.now() + 0;\n",
" window._bokeh_failed_load = false;\n",
" }\n",
" \n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
" \n",
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" }\n",
" }\n",
" \n",
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" }\n",
" \n",
" function load_libs(js_urls, callback) {\n",
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" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
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" s.onerror = function() {\n",
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" \n",
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" \n",
" var inline_js = [\n",
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" var render_items = [{\"docid\":\"0dd614dd-3db3-4043-ab1b-7d74b5a963d7\",\"elementid\":\"a4a8bae1-c444-4dab-873d-10e91ed15d6f\",\"modelid\":\"bbbedbbc-7c7d-4c77-a5f3-4cec5f1196d8\"}];\n",
" \n",
" Bokeh.embed.embed_items(docs_json, render_items);\n",
" };\n",
" if (document.readyState != \"loading\") fn();\n",
" else document.addEventListener(\"DOMContentLoaded\", fn);\n",
" })();\n",
" },\n",
" function(Bokeh) {\n",
" }\n",
" ];\n",
" \n",
" function run_inline_js() {\n",
" \n",
" if ((window.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!window._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" window._bokeh_failed_load = true;\n",
" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"a4a8bae1-c444-4dab-873d-10e91ed15d6f\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
" \n",
" }\n",
" \n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
" }(this));\n",
"</script>"
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"source": [
"# add our DataFrame as a ColumnDataSource for Bokeh\n",
"plot_data = ColumnDataSource(tsne_vectors)\n",
"\n",
"# create the plot and configure the\n",
"# title, dimensions, and tools\n",
"tsne_plot = figure(title='t-SNE Word Embeddings',\n",
" plot_width = 800,\n",
" plot_height = 800,\n",
" tools= ('pan, wheel_zoom, box_zoom,'\n",
" 'box_select, resize, reset'),\n",
" active_scroll='wheel_zoom')\n",
"\n",
"# add a hover tool to display words on roll-over\n",
"tsne_plot.add_tools( HoverTool(tooltips = '@word') )\n",
"\n",
"# draw the words as circles on the plot\n",
"tsne_plot.circle('x_coord', 'y_coord', source=plot_data,\n",
" color='blue', line_alpha=0.2, fill_alpha=0.1,\n",
" size=10, hover_line_color='black')\n",
"\n",
"# configure visual elements of the plot\n",
"tsne_plot.title.text_font_size = value('16pt')\n",
"tsne_plot.xaxis.visible = False\n",
"tsne_plot.yaxis.visible = False\n",
"tsne_plot.grid.grid_line_color = None\n",
"tsne_plot.outline_line_color = None\n",
"\n",
"# engage!\n",
"show(tsne_plot);"
]
},
{
"cell_type": "markdown",
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"source": [
"## Conclusion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Whew! Let's round up the major components that we've seen:\n",
"1. Text processing with **spaCy**\n",
"1. Automated **phrase modeling**\n",
"1. Topic modeling with **LDA** $\\ \\longrightarrow\\ $ visualization with **pyLDAvis**\n",
"1. Word vector modeling with **word2vec** $\\ \\longrightarrow\\ $ visualization with **t-SNE**\n",
"\n",
"#### Why use these models?\n",
"Dense vector representations for text like LDA and word2vec can greatly improve performance for a number of common, text-heavy problems like:\n",
"- Text classification\n",
"- Search\n",
"- Recommendations\n",
"- Question answering"
]
},
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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