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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda3\\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": [
"import pandas as pd\n",
"import sqlite3\n",
"import gensim\n",
"import nltk\n",
"import glob\n",
"import json\n",
"import pickle\n",
"\n",
"## Helpers\n",
"\n",
"def save_pkl(target_object, filename):\n",
" with open(filename, \"wb\") as file:\n",
" pickle.dump(target_object, file)\n",
" \n",
"def load_pkl(filename):\n",
" return pickle.load(open(filename, \"rb\"))\n",
"\n",
"def save_json(target_object, filename):\n",
" with open(filename, 'w') as file:\n",
" json.dump(target_object, file)\n",
" \n",
"def load_json(filename):\n",
" with open(filename, 'r') as file:\n",
" data = json.load(file)\n",
" return data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preparing Data\n",
"\n",
"In this step, we are going to load data from disk to the memory and properly format them so that we can processing them in the next \"preprocessing\" stage."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <th>abstract</th>\n",
" <th>publication_date</th>\n",
" <th>submission_date</th>\n",
" <th>cover_url</th>\n",
" <th>full_url</th>\n",
" <th>first_page</th>\n",
" <th>last_page</th>\n",
" <th>pages</th>\n",
" <th>document_type</th>\n",
" <th>type</th>\n",
" <th>article_id</th>\n",
" <th>context_key</th>\n",
" <th>label</th>\n",
" <th>publication_title</th>\n",
" <th>submission_path</th>\n",
" <th>journal_id</th>\n",
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" <th>0</th>\n",
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" <td>2</td>\n",
" <td>Flexible Learning and Academic Performance in ...</td>\n",
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" <td>2009-02-26T22:04:53-08:00</td>\n",
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" <th>2</th>\n",
" <td>3</td>\n",
" <td>Proactive Metrics: A Framework for Managing IS...</td>\n",
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" <td>2001-01-01T00:00:00-08:00</td>\n",
" <td>2009-02-26T22:03:31-08:00</td>\n",
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" <td>acis2001/12</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
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" <td>&lt;p&gt;We describe the Prescriptive Simplified Met...</td>\n",
" <td>2001-01-01T00:00:00-08:00</td>\n",
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"text/plain": [
" id title \\\n",
"0 1 Role-play and Use Case Cards for Requirements ... \n",
"1 2 Flexible Learning and Academic Performance in ... \n",
"2 3 Proactive Metrics: A Framework for Managing IS... \n",
"3 4 Reuse in Information Systems Development: Clas... \n",
"4 5 Improving Software Development: The Prescripti... \n",
"\n",
" abstract \\\n",
"0 <p>This paper presents a technique that uses r... \n",
"1 <p>This research investigates the effectivenes... \n",
"2 <p>Managers of information systems development... \n",
"3 <p>There has been a trend in recent years towa... \n",
"4 <p>We describe the Prescriptive Simplified Met... \n",
"\n",
" publication_date submission_date \\\n",
"0 2006-01-01T00:00:00-08:00 2009-02-26T07:42:10-08:00 \n",
"1 2001-01-01T00:00:00-08:00 2009-02-26T22:04:53-08:00 \n",
"2 2001-01-01T00:00:00-08:00 2009-02-26T22:03:31-08:00 \n",
"3 2001-01-01T00:00:00-08:00 2009-02-26T22:02:29-08:00 \n",
"4 2001-01-01T00:00:00-08:00 2009-02-26T22:01:24-08:00 \n",
"\n",
" cover_url \\\n",
"0 http://aisel.aisnet.org/acis2001/1 \n",
"1 http://aisel.aisnet.org/acis2001/10 \n",
"2 http://aisel.aisnet.org/acis2001/11 \n",
"3 http://aisel.aisnet.org/acis2001/12 \n",
"4 http://aisel.aisnet.org/acis2001/13 \n",
"\n",
" full_url first_page last_page \\\n",
"0 http://aisel.aisnet.org/cgi/viewcontent.cgi?ar... \n",
"1 http://aisel.aisnet.org/cgi/viewcontent.cgi?ar... \n",
"2 http://aisel.aisnet.org/cgi/viewcontent.cgi?ar... \n",
"3 http://aisel.aisnet.org/cgi/viewcontent.cgi?ar... \n",
"4 http://aisel.aisnet.org/cgi/viewcontent.cgi?ar... \n",
"\n",
" pages document_type type article_id context_key label \\\n",
"0 article article 1001 742028 1 \n",
"1 article article 1006 744077 10 \n",
"2 article article 1005 744076 11 \n",
"3 article article 1004 744075 12 \n",
"4 article article 1003 744074 13 \n",
"\n",
" publication_title submission_path journal_id \n",
"0 ACIS 2001 Proceedings acis2001/1 1 \n",
"1 ACIS 2001 Proceedings acis2001/10 2 \n",
"2 ACIS 2001 Proceedings acis2001/11 3 \n",
"3 ACIS 2001 Proceedings acis2001/12 4 \n",
"4 ACIS 2001 Proceedings acis2001/13 5 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Loading metadata from trainning database\n",
"con = sqlite3.connect(\"F:/FMR/data.sqlite\")\n",
"db_documents = pd.read_sql_query(\"SELECT * from documents\", con)\n",
"db_authors = pd.read_sql_query(\"SELECT * from authors\", con)\n",
"data = db_documents # just a handy alias\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading Tokenised Full Text\n",
"\n",
"In the previous tutorial (Jupyter notebook), we generated a bunch of .json files storing our tokenised full texts. Now we are going to load them."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tokenised_part = glob.glob(\"F:/FMR/np-*\")\n",
"tokenised = {}\n",
"for file in tokenised_part:\n",
" tokenised = {**tokenised, **load_json(file)}"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['role-play',\n",
" 'case',\n",
" 'requirements review',\n",
" 'uses role-play',\n",
" 'index cards',\n",
" 'case development',\n",
" 'crc',\n",
" 'card technique',\n",
" 'essential use cases',\n",
" 'index cards']"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's have a peek\n",
"tokenised[\"acis2001/1\"][:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preprocessing Data for Gensim and Finetuning\n",
"\n",
"In this stage, we preprocess the data so it could be read by Gensim. Then we will furthur clean up the data to better train the model.\n",
"\n",
"First of all, we need a dictionary of our corpus, i.e., the whole collection of our full texts. However, there are documents in our dataset written in some other languages. We need to stay with one language (in the example, English) in order to best train the model, so let's filter them out first.\n",
"\n",
"## Language Detection\n",
"\n",
"`TextBlob` ships with a handy API wrapper of Google's language detection service. We will store the `id` of these non-English documents in a list called `non_en` and save it as a pickled file for later use. "
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from textblob import TextBlob\n",
"non_en = [] # a list of ids of the documents in other languages\n",
"count = 0\n",
"for id_, entry in data.iterrows():\n",
" count += 1\n",
" try:\n",
" lang = TextBlob(entry[\"title\"] + \" \" + entry[\"abstract\"]).detect_language()\n",
" except:\n",
" raise\n",
" if lang != 'en':\n",
" non_en.append(id_)\n",
" print(lang, data.iloc[id_][\"title\"])\n",
" if (count % 100) == 0:\n",
" print(\"Progress: \", count)\n",
"\n",
"save_pkl(non_en, \"non_en.list.pkl\")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"non_en = load_pkl(\"non_en.list.pkl\")"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Convert our dict-based structure to be a list-based structure that are readable by Gensim and at the same time, filter out those non-English documents\n",
"tokenised_list = [tokenised[i] for i in data[\"submission_path\"] if i not in non_en]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Although we tried to handle these hyphenations in the previous tutorial, now we still have them for some reasons. The most conveient way to remove them is to remove them in the corpus and rebuild the dictionary. Then re-apply our previous filter."
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def remove_hyphenation(l):\n",
" return [i.replace(\"- \", \"\").replace(\"-\", \"\") for i in l]\n",
"tokenised_list = [remove_hyphenation(i) for i in tokenised_list]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lematization\n",
"\n",
"But before building the vocabulary, we need to unify some variants of the same phrases. For example, \"technologies\" should be mapped to \"technology\". This process is called lemmatization."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'assistive technology'"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\" \".join([lemmatizer.lemmatize(token)\n",
" for token\n",
" in 'assistive technologies'.split(\" \")])"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from nltk.stem.wordnet import WordNetLemmatizer\n",
"lemmatizer = WordNetLemmatizer()\n",
"def lemmatize(l):\n",
" return [\" \".join([lemmatizer.lemmatize(token)\n",
" for token\n",
" in phrase.split(\" \")])\n",
" for phrase in l]\n",
"\n",
"def lemmatize_all(tokenised):\n",
" # Lemmatize the documents.\n",
" lemmatized = [lemmatize(entry) for entry in tokenised]\n",
" return lemmatized"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tokenised_list = lemmatize_all(tokenised_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we can create our lemmatized vocabulary."
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from gensim.corpora import Dictionary\n",
"\n",
"# Create a dictionary for all the documents. This might take a while.\n",
"dictionary = Dictionary(tokenised_list)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'m. cheung'"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's see what's inside, note the spelling :)\n",
"# But there is really nothing we can do with that.\n",
"dictionary[3]"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2866937"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dictionary)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Obviously we have a way too large vocabulary size. This is because the algorithm used in TextBlob's noun phrase extraction is not very robust in complicated scenario. Let's see what we can do about this.\n",
"\n",
"## Filtering Vocabulary\n",
"\n",
"First of all, let's rule out the most obvious ones: words and phrases that appear in too many documents and ones that appear only 1-5 documents. Gensim provides a very convenient built-in function to filter them out:"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# remove tokens that appear in less than 20 documents and tokens that appear in more than 50% of the documents.\n",
"dictionary.filter_extremes(no_below=20, no_above=0.5, keep_n=None)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"99375"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dictionary)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we have drastically reduced the size of the vocabulary from 2936116 to 102508. However this is not enough. For example:"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Helpers\n",
"display_limit = 10\n",
"def shorter_than(n):\n",
" bad = []\n",
" count = 0\n",
" for i in dictionary:\n",
" if len(dictionary[i]) < n:\n",
" count += 1\n",
" if count < display_limit:\n",
" print(dictionary[i])\n",
" bad.append(i)\n",
" print(count)\n",
" return bad\n",
"\n",
"def if_in(symbol):\n",
" bad = []\n",
" count = 0\n",
" for i in dictionary:\n",
" if symbol in dictionary[i]:\n",
" count += 1\n",
" if count < display_limit:\n",
" print(dictionary[i])\n",
" bad.append(i)\n",
" print(count)\n",
" return bad\n",
"\n",
"def more_than(symbol, n):\n",
" bad = []\n",
" count = 0\n",
" for i in dictionary:\n",
" if dictionary[i].count(symbol) > n:\n",
" count += 1\n",
" if count < display_limit:\n",
" print(dictionary[i])\n",
" bad.append(i)\n",
" print(count)\n",
" return bad"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x7\n",
"gi\n",
"fm\n",
"gb\n",
"cb\n",
"so\n",
"dw\n",
"l.\n",
"hf\n",
"769\n"
]
}
],
"source": [
"bad = shorter_than(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have 752 such meaningless tokens in our vocabulary. Presumably this is because that during the extraction of the PDF, some mathenmatical equations are parsed as plain text (of course).\n",
"\n",
"Now we are going to remove these:"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dictionary.filter_tokens(bad_ids=bad)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p h\n",
"raum\n",
"h.w\n",
"mell\n",
"gpa\n",
"wom\n",
"shi\n",
"mil\n",
"belz\n",
"eul\n",
"pine\n",
"rq2\n",
"pahl\n",
"f.y.\n",
"tape\n",
"cba\n",
"b t\n",
"tbp\n",
"p4b\n",
"> >\n",
"k j\n",
"hew\n",
"iam\n",
"ill.\n",
"f.y\n",
"typ\n",
"barr\n",
"soho\n",
"time\n",
"falk\n",
"cow\n",
"v.j.\n",
"sri\n",
"weg\n",
"rol\n",
"kqml\n",
"as2\n",
"logo\n",
"foxx\n",
"sec\n",
"amos\n",
"j i\n",
"ehr\n",
"uso\n",
"ott\n",
"esa\n",
"i.1\n",
"kirs\n",
"tong\n",
"n h\n",
"jian\n",
"roa\n",
"sure\n",
"lucy\n",
"ifm\n",
"b.h\n",
"m.y.\n",
"with\n",
"t.h\n",
"cnpq\n",
"agre\n",
"beam\n",
"odom\n",
"dexa\n",
"efqm\n",
"+ t\n",
"seth\n",
"= }\n",
"j.w\n",
"rsq\n",
"w.g.\n",
"kbps\n",
"test\n",
"ing\n",
"ijec\n",
"$ =\n",
"frew\n",
"eff\n",
"cop\n",
"zeng\n",
"u.c\n",
"ihde\n",
"e.c\n",
"pidd\n",
"auto\n",
"acm\n",
"lcc\n",
"grn\n",
"com2\n",
"[ [\n",
"p >\n",
"der\n",
"ohio\n",
"cai\n",
"tsp\n",
"wybo\n",
"bsc\n",
"s.f.\n",
"zang\n",
"6400\n"
]
}
],
"source": [
"display_limit = 100\n",
"bad = shorter_than(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are more to be filtered out. It is tricky to determine the minimum length of the token, since some abbrivations may be as short as these noises too. Let's go with 5 in this tutorial. Some other noises are shown below:"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dictionary.filter_tokens(bad_ids=bad)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"*** p < .001\n",
"***p <\n",
"* p < .10\n",
"** p <\n",
"* * * *\n",
"** p < .01\n",
"* denotes\n",
"level **\n",
"**p < .05\n",
"34\n"
]
}
],
"source": [
"display_limit = 10\n",
"bad = if_in(\"*\")"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dictionary.filter_tokens(bad_ids=bad)"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"strong > < /strong >\n",
"< http\n",
"pvalue <\n",
"p < .005\n",
"p < .1\n",
"p < .10\n",
"< .001\n",
"year <\n",
"/ > <\n",
"28\n"
]
}
],
"source": [
"bad = if_in(\"<\")"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dictionary.filter_tokens(bad_ids=bad)"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"year >\n",
"> .70\n",
"value >\n",
"> |t|\n",
"strong >\n",
"p > .1\n",
"eigenvalue >\n",
"p > .05\n",
"> .05\n",
"10\n"
]
}
],
"source": [
"bad = if_in(\">\")"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dictionary.filter_tokens(bad_ids=bad)"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"% volume\n",
"% work\n",
"% female\n",
"% threshold\n",
"% variance\n",
"% market share\n",
"% report\n",
"% success rate\n",
"% level\n",
"59\n"
]
}
],
"source": [
"bad = if_in(\"%\")"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dictionary.filter_tokens(bad_ids=bad)"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"acm/ais\n",
"business /\n",
"product/service quality\n",
"his/her\n",
"ieee/acis\n",
"he/she perceives\n",
"products/services\n",
"organization and/or\n",
"his/her knowledge\n",
"212\n"
]
}
],
"source": [
"bad = if_in(\"/\")"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dictionary.filter_tokens(bad_ids=bad)"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"285. http\n",
"b. han\n",
"z. zhang\n",
"j. webster\n",
"p. s. goodman\n",
"j. e. group\n",
"basten @ wiso.unikoeln.de\n",
"t. goles\n",
"t. weitzel\n",
"3029\n"
]
}
],
"source": [
"bad = if_in(\".\")"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dictionary.filter_tokens(bad_ids=bad)"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"characteristic [\n",
"power [\n",
"professional [\n",
"test [\n",
"code [\n",
"component [\n",
"service provider [\n",
"good [\n",
"concept [\n",
"404\n",
"2002a ]\n",
"business ]\n",
"] defines\n",
"] beschrieben\n",
"group ]\n",
"] need\n",
"] cite\n",
"] estimate\n",
"2004b ]\n",
"130\n",
"0\n",
"0\n"
]
}
],
"source": [
"bad = if_in(\"[\")\n",
"bad += if_in(\"]\")\n",
"bad += if_in(\"}\")\n",
"bad += if_in(\"{\")\n",
"dictionary.filter_tokens(bad_ids=bad)"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"about the author steven alter\n",
"conclusion and future research direction\n",
"usa dave chatterjee mike chiasson jan damsgaard chris forman\n",
"w h e r e\n",
"c t i o n\n",
"n r e t x\n",
"specific computer selfefficacyan empirical assessment\n",
"hong kong ashley bush florida\n",
"l y s i s\n",
"information system outsourcing relationship factor\n",
"china administrative personnel eph mclean ai\n",
"d u r c h\n",
"t i o n s\n",
"n d i v i d u\n",
"n d c o m m u n i c\n",
"hong kong mary granger george washington\n",
"s e d o n\n",
"palabras claves y resumen debern ser incluidos\n",
"ai publication committee virpi tuunainen\n",
"398\n"
]
}
],
"source": [
"display_limit = 20\n",
"bad = more_than(\" \", 3)\n",
"dictionary.filter_tokens(bad_ids=bad)"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"usa jiye mao indiana\n",
"seev neumann tel aviv\n",
"e v i t\n",
"explaining software developer acceptance\n",
"positivist case research methodology\n",
"usa markku saaksjarvi helsinki\n",
"n i t i\n",
"information system development methodology\n",
"t h e m\n",
"+ + + +\n",
"a4 mi quarterly vol\n",
"oulu sal march vanderbilt\n",
"i e s e\n",
"kde developer mailing list\n",
"j manag inf syst\n",
"research design and methodology\n",
"e r c n\n",
"global software development team\n",
"usa youngjin yoo case\n",
"784\n"
]
}
],
"source": [
"display_limit = 20\n",
"bad = more_than(\" \", 2)\n",
"dictionary.filter_tokens(bad_ids=bad)"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n"
]
}
],
"source": [
"bad = if_in(\"- \")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Removing Names\n",
"\n",
"There are a lot of citations and references in the PDFs, and they are extremely difficult to be recoginsed given that they come in a lot of variants.\n",
"\n",
"We will demostrate how to identify these names in another tutorial (see TOC) using a Stanford NLP library, and eventually we can get a list of names in `names_cleared.json`."
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"names = load_json(\"../names_cleared.json\")\n",
"name_ids = [i for i, v in dictionary.iteritems() if v in names]"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dictionary.filter_tokens(bad_ids=name_ids)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Building Corpus in Gensim Format\n",
"\n",
"Since we already have a dictionary, each distinct token can be expressed as a *id* in the dictionary. Then we can compress the Corpus using this new representation and convert the document to be a BoW (bag of words)."
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"corpus = [dictionary.doc2bow(l) for l in tokenised_list]"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Save it for future usage\n",
"from gensim.corpora.mmcorpus import MmCorpus\n",
"MmCorpus.serialize(\"aisnet_cleaned.mm\", corpus)"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Also save the dictionary\n",
"dictionary.save(\"aisnet_300_cleaned.ldamodel.dictionary\")"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# To load the corpus:\n",
"from gensim.corpora.mmcorpus import MmCorpus\n",
"corpus = MmCorpus(\"aisnet_cleaned.mm\")\n",
"# To load the dictionary:\n",
"from gensim.corpora import Dictionary\n",
"dictionary = Dictionary.load(\"aisnet_300_cleaned.ldamodel.dictionary\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train the LDA Model\n",
"\n",
"Now we have the dictionary and the corpus, we are ready to train our LDA model. We take the LDA model with 300 topics for example."
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dictionary test: table 6a\n"
]
}
],
"source": [
"# Train LDA model.\n",
"from gensim.models import LdaModel\n",
"\n",
"# Set training parameters.\n",
"num_topics = 300\n",
"chunksize = 2000\n",
"passes = 1\n",
"iterations = 150\n",
"eval_every = None # Don't evaluate model perplexity, takes too much time.\n",
"\n",
"# Make a index to word dictionary.\n",
"print(\"Dictionary test: \" + dictionary[0]) # This is only to \"load\" the dictionary.\n",
"id2word = dictionary.id2token\n",
"\n",
"model = LdaModel(corpus=corpus, id2word=id2word, chunksize=chunksize, \\\n",
" alpha='auto', eta='auto', \\\n",
" iterations=iterations, num_topics=num_topics, \\\n",
" passes=passes, eval_every=eval_every)"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Save the LDA model\n",
"model.save(\"aisnet_300_cleaned.ldamodel\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualize the LDA Model\n",
"\n",
"There is a convenient library called `pyLDAvis` that allows us to visualize our trained LDA model."
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pyLDAvis.gensim\n",
"vis = pyLDAvis.gensim.prepare(model, corpus, dictionary)"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": false
},
"outputs": [
{
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