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invegat/twitter-nlp-classifiers.ipynb

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twitter-nlp-classifiers.ipynb
{
"nbformat": 4,
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"metadata": {
"colab": {
"name": "Twitter NLP Classifiers.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autoclose": false,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 1,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
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"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/invegat/bb437d222f9d8eed2b07fa4f10247bfc/twitter-nlp-classifiers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "p84xDZIlsQ9J",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 66
},
"outputId": "aae3e04b-1a0b-4c5a-8998-138116d29c29"
},
"source": [
"import nltk\n",
"nltk.download('stopwords')"
],
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {
"tags": []
},
"execution_count": 29
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Uf6JRflWq1cz",
"colab_type": "code",
"colab": {}
},
"source": [
"import re\n",
"import io\n",
"import pandas as pd\n",
"import requests\n",
"import gensim\n",
"from gensim.models.word2vec import Word2Vec\n",
"from nltk.tokenize import word_tokenize\n",
"import string\n",
"from nltk.corpus import stopwords\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from xgboost import XGBClassifier\n",
"from sklearn.metrics import accuracy_score, roc_auc_score\n",
"from sklearn.linear_model import LogisticRegression"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2019-03-29T17:50:36.169063Z",
"start_time": "2019-03-29T17:50:36.165101Z"
},
"id": "Fg-goHQyqOuj",
"colab_type": "code",
"colab": {}
},
"source": [
"url = \"https://raw.githubusercontent.com/ryanleeallred/datasets/master/twitter_sentiment_binary.csv\""
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2019-03-29T17:50:39.124431Z",
"start_time": "2019-03-29T17:50:36.698578Z"
},
"id": "TZ_jEGwNqOuo",
"colab_type": "code",
"colab": {}
},
"source": [
"text = requests.get(url).text"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2019-03-29T17:50:42.867912Z",
"start_time": "2019-03-29T17:50:39.129493Z"
},
"id": "bvcSi4PuqOus",
"colab_type": "code",
"colab": {}
},
"source": [
"df = pd.read_csv(url)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2019-03-29T17:50:45.513536Z",
"start_time": "2019-03-29T17:50:42.869797Z"
},
"id": "gPNbgehNqOux",
"colab_type": "code",
"outputId": "03a945d3-d2cb-4b02-8808-2adfad990467",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 214
}
},
"source": [
"stop_words = set(stopwords.words('english'))\n",
"\n",
"# turn a doc into clean tokens\n",
"def clean_doc(doc):\n",
"\t# split into tokens by white space\n",
"\ttokens = doc.split()\n",
"\t# remove punctuation from each token\n",
"\ttable = str.maketrans('', '', string.punctuation)\n",
"\ttokens = [w.translate(table) for w in tokens]\n",
"\t# remove remaining tokens that are not alphabetic\n",
"\ttokens = [word for word in tokens if word.isalpha()]\n",
"\t# filter out stop words\n",
"# \tstop_words = set(stopwords.words('english'))\n",
"\ttokens = [w for w in tokens if not w in stop_words]\n",
"\t# filter out short tokens\n",
"\ttokens = [word.lower() for word in tokens if len(word) > 1]\n",
"\treturn tokens\n",
"\n",
"def clean_sentence(doc):\n",
"\t# split into tokens by white space\n",
"\ttokens = doc.split()\n",
"\t# remove punctuation from each token\n",
"\ttable = str.maketrans('', '', string.punctuation)\n",
"\ttokens = [w.translate(table) for w in tokens]\n",
"\t# remove remaining tokens that are not alphabetic\n",
"\ttokens = [word for word in tokens if word.isalpha()]\n",
"\t# filter out stop words\n",
"# \tstop_words = set(stopwords.words('english'))\n",
"\ttokens = [w for w in tokens if not w in stop_words]\n",
"\t# filter out short tokens\n",
"\ttokens = [word.lower() for word in tokens if len(word) > 1]\n",
"\treturn \" \".join(tokens)\n",
"\n",
"df['cleaned'] = df.SentimentText.apply(clean_doc)\n",
"df['cleanedSentence'] = df.SentimentText.apply(clean_sentence)\n",
"print(df.shape)\n",
"df.head()"
],
"execution_count": 34,
"outputs": [
{
"output_type": "stream",
"text": [
"(99989, 4)\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sentiment</th>\n",
" <th>SentimentText</th>\n",
" <th>cleaned</th>\n",
" <th>cleanedSentence</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>is so sad for my APL frie...</td>\n",
" <td>[sad, apl, friend]</td>\n",
" <td>sad apl friend</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>I missed the New Moon trail...</td>\n",
" <td>[missed, new, moon, trailer]</td>\n",
" <td>missed new moon trailer</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>omg its already 7:30 :O</td>\n",
" <td>[omg, already]</td>\n",
" <td>omg already</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>.. Omgaga. Im sooo im gunna CRy. I'...</td>\n",
" <td>[omgaga, im, sooo, im, gunna, cry, ive, dentis...</td>\n",
" <td>omgaga im sooo im gunna cry ive dentist since ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>i think mi bf is cheating on me!!! ...</td>\n",
" <td>[think, mi, bf, cheating, tt]</td>\n",
" <td>think mi bf cheating tt</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sentiment ... cleanedSentence\n",
"0 0 ... sad apl friend\n",
"1 0 ... missed new moon trailer\n",
"2 1 ... omg already\n",
"3 0 ... omgaga im sooo im gunna cry ive dentist since ...\n",
"4 0 ... think mi bf cheating tt\n",
"\n",
"[5 rows x 4 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 34
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2019-03-29T17:50:46.634914Z",
"start_time": "2019-03-29T17:50:45.516386Z"
},
"id": "e1t8UfPHqOu2",
"colab_type": "code",
"outputId": "f5cdd776-284c-431c-c5d0-ed083abead75",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 133
}
},
"source": [
"vectorizer = TfidfVectorizer(max_features=10000, ngram_range=(1,1), stop_words='english')\n",
"vectorizer.fit(df.cleanedSentence)"
],
"execution_count": 35,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
" dtype=<class 'numpy.float64'>, encoding='utf-8',\n",
" input='content', lowercase=True, max_df=1.0, max_features=10000,\n",
" min_df=1, ngram_range=(1, 1), norm='l2', preprocessor=None,\n",
" smooth_idf=True, stop_words='english', strip_accents=None,\n",
" sublinear_tf=False, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
" tokenizer=None, use_idf=True, vocabulary=None)"
]
},
"metadata": {
"tags": []
},
"execution_count": 35
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2019-03-29T17:50:54.165799Z",
"start_time": "2019-03-29T17:50:54.162824Z"
},
"id": "urSxxHj2qOu9",
"colab_type": "code",
"colab": {}
},
"source": [
"X_train = df.cleanedSentence"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2019-03-29T17:50:57.723419Z",
"start_time": "2019-03-29T17:50:55.005352Z"
},
"id": "PCvseHfyqOvA",
"colab_type": "code",
"outputId": "2f8fc629-e5e8-4ce1-8a51-57fc22e033d2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 263
}
},
"source": [
"train_word_counts = vectorizer.transform(X_train)\n",
"X_train_vectorized = pd.DataFrame(train_word_counts[0:10000].toarray(), columns=vectorizer.get_feature_names())\n",
"X_train_vectorized = X_train_vectorized.fillna(0)\n",
"print(X_train_vectorized.shape)\n",
"X_train_vectorized.head()"
],
"execution_count": 37,
"outputs": [
{
"output_type": "stream",
"text": [
"(10000, 10000)\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
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" <th>zoom</th>\n",
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" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>...</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 10000 columns</p>\n",
"</div>"
],
"text/plain": [
" aa aaa aaah aaahh aafreen aah ... zomg zone zones zoo zoom zune\n",
"0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0\n",
"1 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0\n",
"2 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0\n",
"3 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0\n",
"4 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0\n",
"\n",
"[5 rows x 10000 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 37
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2019-03-29T17:51:09.809785Z",
"start_time": "2019-03-29T17:51:09.761256Z"
},
"colab_type": "code",
"id": "TX8OEgUP_3ee",
"colab": {}
},
"source": [
"dfs = df.sample(frac=0.1)\n",
"X = dfs.cleanedSentence\n",
"y = dfs.Sentiment.values\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2019-03-29T17:51:11.508193Z",
"start_time": "2019-03-29T17:51:11.504530Z"
},
"id": "h4YsonNhqOvL",
"colab_type": "code",
"outputId": "59a68def-cac0-4e16-9c30-403e7f65db1b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 83
}
},
"source": [
"print(X_train.shape)\n",
"print(X_test.shape)\n",
"print(y_train.shape)\n",
"print(y_test.shape)"
],
"execution_count": 39,
"outputs": [
{
"output_type": "stream",
"text": [
"(7999,)\n",
"(2000,)\n",
"(7999,)\n",
"(2000,)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2019-03-29T17:51:16.482859Z",
"start_time": "2019-03-29T17:51:16.361378Z"
},
"id": "AlvWW5wRqOvP",
"colab_type": "code",
"outputId": "a1b2d96c-4f69-43f0-8f36-a4d8f8dcf099",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 116
}
},
"source": [
"vectorizer = CountVectorizer(max_features=1000, ngram_range=(1,1), stop_words='english')\n",
"vectorizer.fit(X_train)"
],
"execution_count": 40,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
" dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
" lowercase=True, max_df=1.0, max_features=1000, min_df=1,\n",
" ngram_range=(1, 1), preprocessor=None, stop_words='english',\n",
" strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
" tokenizer=None, vocabulary=None)"
]
},
"metadata": {
"tags": []
},
"execution_count": 40
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2019-03-29T17:51:18.167707Z",
"start_time": "2019-03-29T17:51:18.059599Z"
},
"id": "MkQUL4jzqOvW",
"colab_type": "code",
"outputId": "f1146396-5ef0-4133-c5f8-84df0bfd6aad",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 50
}
},
"source": [
"train_word_counts = vectorizer.transform(X_train)\n",
"X_train_vectorized = pd.DataFrame(train_word_counts.toarray(), columns=vectorizer.get_feature_names())\n",
"\n",
"test_word_counts = vectorizer.transform(X_test)\n",
"X_test_vectorized = pd.DataFrame(test_word_counts.toarray(), columns=vectorizer.get_feature_names())\n",
"print(X_train_vectorized.shape)\n",
"print(X_test_vectorized.shape)"
],
"execution_count": 41,
"outputs": [
{
"output_type": "stream",
"text": [
"(7999, 1000)\n",
"(2000, 1000)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2019-03-29T17:51:21.222904Z",
"start_time": "2019-03-29T17:51:21.218619Z"
},
"id": "jIUocZm5qOva",
"colab_type": "code",
"outputId": "d9377399-8851-4a32-c45d-e691a01ded47",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 33
}
},
"source": [
"df.Sentiment.unique()"
],
"execution_count": 42,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0, 1])"
]
},
"metadata": {
"tags": []
},
"execution_count": 42
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2019-03-29T17:48:51.962715Z",
"start_time": "2019-03-29T17:48:31.500744Z"
},
"id": "acEs8nlnqOve",
"colab_type": "code",
"outputId": "c05aa698-dfcf-4a65-a877-8b30f244d7a1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 83
}
},
"source": [
"XGB = XGBClassifier(n_estimators=200, objective=\"binary:logistic\").fit(X_train_vectorized, y_train)\n",
"train_predictions = XGB.predict(X_train_vectorized)\n",
"test_predictions = XGB.predict(X_test_vectorized)\n",
"print(f'Train Accuracy: {accuracy_score(y_train, train_predictions)}')\n",
"print(f'Test Accuracy: {accuracy_score(y_test, test_predictions)}')\n",
"print(f'Train Roc Auc: {roc_auc_score(y_train, train_predictions)}')\n",
"print(f'Test Roc Auc: {roc_auc_score(y_test, test_predictions)}')"
],
"execution_count": 43,
"outputs": [
{
"output_type": "stream",
"text": [
"Train Accuracy: 0.700962620327541\n",
"Test Accuracy: 0.6615\n",
"Train Roc Auc: 0.6712842399415706\n",
"Test Roc Auc: 0.6423723426511531\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2019-03-29T17:49:33.866072Z",
"start_time": "2019-03-29T17:48:53.730994Z"
},
"id": "5JIC_D9hqOvi",
"colab_type": "code",
"outputId": "9efa2958-961a-439e-e5b5-48e76e0a3d2a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 83
}
},
"source": [
"RFC = RandomForestClassifier(n_estimators=200).fit(X_train_vectorized, y_train)\n",
"\n",
"train_predictions = RFC.predict(X_train_vectorized)\n",
"test_predictions = RFC.predict(X_test_vectorized)\n",
"\n",
"print(f'Train Accuracy: {accuracy_score(y_train, train_predictions)}')\n",
"print(f'Test Accuracy: {accuracy_score(y_test, test_predictions)}')\n",
"print(f'Train Roc Auc: {roc_auc_score(y_train, train_predictions)}')\n",
"print(f'Test Roc Auc: {roc_auc_score(y_test, test_predictions)}')"
],
"execution_count": 44,
"outputs": [
{
"output_type": "stream",
"text": [
"Train Accuracy: 0.9577447180897612\n",
"Test Accuracy: 0.6755\n",
"Train Roc Auc: 0.9540209266139531\n",
"Test Roc Auc: 0.670136709634851\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2019-03-29T17:49:38.271311Z",
"start_time": "2019-03-29T17:49:36.806563Z"
},
"id": "KWeYS1RuqOvo",
"colab_type": "code",
"outputId": "c08a65cf-487c-4c26-8f0d-168b6d223d58",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 83
}
},
"source": [
"LR = LogisticRegression(random_state=42, solver=\"newton-cg\").fit(X_train_vectorized, y_train)\n",
"\n",
"train_predictions = LR.predict(X_train_vectorized)\n",
"test_predictions = LR.predict(X_test_vectorized)\n",
"print(f'Train Accuracy: {accuracy_score(y_train, train_predictions)}')\n",
"print(f'Test Accuracy: {accuracy_score(y_test, test_predictions)}')\n",
"print(f'Train Roc Auc: {roc_auc_score(y_train, train_predictions)}')\n",
"print(f'Test Roc Auc: {roc_auc_score(y_test, test_predictions)}')"
],
"execution_count": 45,
"outputs": [
{
"output_type": "stream",
"text": [
"Train Accuracy: 0.7727215901987748\n",
"Test Accuracy: 0.687\n",
"Train Roc Auc: 0.7609365215458385\n",
"Test Roc Auc: 0.6786840792417\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6thJvUHBqOwD",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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