From 0bedbfba4f2c4925a4b911832084d1d6a1ade7d1 Mon Sep 17 00:00:00 2001 From: SOUMYO NATH TRIPATHY <85414445+SoumyoNathTripathy@users.noreply.github.com> Date: Thu, 10 Feb 2022 13:14:03 +0530 Subject: [PATCH 1/2] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 25281db..c3c9a98 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ This a website that will take the name of any company as an input and fetches all the news regarding that company using GoogleNews. -It then does sentiment analysis of the news fetched and gives a proper sentiment score +It then does Sentiment Analysis of the news fetched and gives a proper Sentiment score. ## The Flow From eefcae0c5fad38668344be111d88d8f103cf35ec Mon Sep 17 00:00:00 2001 From: SOUMYO NATH TRIPATHY <85414445+SoumyoNathTripathy@users.noreply.github.com> Date: Sat, 19 Feb 2022 02:25:56 +0530 Subject: [PATCH 2/2] Add files via upload --- FakeNewsClassifier TFIDF.ipynb | 2615 ++++++++++++++++++++++++++++++++ 1 file changed, 2615 insertions(+) create mode 100644 FakeNewsClassifier TFIDF.ipynb diff --git a/FakeNewsClassifier TFIDF.ipynb b/FakeNewsClassifier TFIDF.ipynb new file mode 100644 index 0000000..8996e98 --- /dev/null +++ b/FakeNewsClassifier TFIDF.ipynb @@ -0,0 +1,2615 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fake News Classifier\n", + "Dataset: https://www.kaggle.com/c/fake-news/data#" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "df=pd.read_csv('fake-news/train.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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22Why the Truth Might Get You FiredConsortiumnews.comWhy the Truth Might Get You Fired October 29, ...1
3315 Civilians Killed In Single US Airstrike Hav...Jessica PurkissVideos 15 Civilians Killed In Single US Airstr...1
44Iranian woman jailed for fictional unpublished...Howard PortnoyPrint \\nAn Iranian woman has been sentenced to...1
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22Why the Truth Might Get You FiredConsortiumnews.comWhy the Truth Might Get You Fired October 29, ...
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44Iranian woman jailed for fictional unpublished...Howard PortnoyPrint \\nAn Iranian woman has been sentenced to...
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Mr. Hamon appeared to have won by a wide margin, with incomplete returns showing him with an estimated 58 percent of the vote to Mr. Valls’s 41 percent. “Tonight the left holds its head up high again it is looking to the future,” Mr. Hamon said, addressing his supporters. “Our country needs the left, but a modern, innovative left,” he said. Mr. Hamon’s victory was the clearest sign yet that voters on the left want a break with the policies of President François Hollande, who in December announced that he would not seek . However, Mr. Hamon’s strong showing is unlikely to change widespread assessments that candidates have little chance of making it into the second round of voting in the general election. The first round of the general election is set for April 23 and the runoff for May 7. The Socialist Party is deeply divided, and one measure of its lack of popular enthusiasm was the relatively low number of people voting. About two million people voted in the second round of the primary on Sunday, in contrast with about 2. 9 million in the second round of the last presidential primary on the left, in 2011. However, much of the conventional wisdom over how the elections will go has been thrown into question over the past week, because the leading candidate, François Fillon, who represents the main party, the Republicans, was accused of paying his wife large sums of money to work as his parliamentary aide. While nepotism is legal in the French political system, it is not clear that she actually did any work. Prosecutors who specialize in financial malfeasance are reviewing the case. France’s electoral system allows multiple candidates to run for president in the first round of voting, but only the top two go on to a second round. Mr. Hamon is entering a race that is already crowded on the left, with candidates who include Mélenchon on the far left, and Emmanuel Macron, an independent who served as economy minister in Mr. Hollande’s government and who embraces more policies. Unless he decides to withdraw, Mr. Fillon, the mainstream right candidate, will also run, as will the extreme right candidate Marine Le Pen. The two have been expected to go to the runoff. Mr. Hamon’s victory can be attributed at least in part to his image as an idealist and traditional leftist candidate who appeals to union voters as well as more environmentally concerned and socially liberal young people. Unlike Mr. Valls, he also clearly distanced himself from some of Mr. Hollande’s more unpopular policies, especially the economic ones. Thomas Kekenbosch, 22, a student and one of the leaders of the group the Youth With Benoît Hamon, said Mr. Hamon embodied a new hope for those on the left. “We have a perspective we have something to do, to build,” Mr. Kekenbosch said. Mr. Hollande had disappointed many young people because under him the party abandoned ideals, such as support for workers, that many voters believe in, according to Mr. Kekenbosch. Mr. Hollande’s government, under pressure from the European Union to meet budget restraints, struggled to pass labor code reforms to make the market more attractive to foreign investors and also to encourage French businesses to expand in France. The measures ultimately passed after weeks of strikes, but they were watered down and generated little concrete progress in improving France’s roughly 10 percent unemployment rate and its nearly 25 percent youth joblessness rate. Mr. Hamon strongly endorses a stimulus approach to improving the economy and has promised to phase in a universal income, which would especially help young people looking for work, but would also supplement the livelihood of French workers. The end goal would be to have everyone receive 750 euros per month (about $840). “We have someone that trusts us,” Mr. Kekenbosch said, “who says: ‘I give you enough to pay for your studies. You can have a scholarship which spares you from working at McDonald’s on provisional contracts for 4 years. ” Mr. Hamon advocates phasing out diesel fuel and encouraging drivers to replace vehicles that use petroleum products with electrical ones. His leftist pedigree began early. His father worked at an arsenal in Brest, a city in the far west of Brittany, and his mother worked off and on as a secretary. He was an early member of the Movement of Young Socialists, and he has continued to work closely with them through his political life. He also worked for Martine Aubry, now the mayor of Lille and a former Socialist Party leader.'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "messages['text'][6]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from nltk.corpus import stopwords\n", + "from nltk.stem.porter import PorterStemmer\n", + "import re\n", + "ps = PorterStemmer()\n", + "corpus = []\n", + "for i in range(0, len(messages)):\n", + " review = re.sub('[^a-zA-Z]', ' ', messages['text'][i])\n", + " review = review.lower()\n", + " review = review.split()\n", + " \n", + " review = [ps.stem(word) for word in review if not word in stopwords.words('english')]\n", + " review = ' '.join(review)\n", + " corpus.append(review)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'civilian kill singl us airstrik identifi'" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "corpus[3]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "## TFidf Vectorizer\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "tfidf_v=TfidfVectorizer(max_features=5000,ngram_range=(1,3))\n", + "X=tfidf_v.fit_transform(corpus).toarray()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18285, 5000)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "y=messages['label']" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "## Divide the dataset into Train and Test\n", + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['abandon',\n", + " 'abc',\n", + " 'abc news',\n", + " 'abduct',\n", + " 'abe',\n", + " 'abedin',\n", + " 'abl',\n", + " 'abort',\n", + " 'abroad',\n", + " 'absolut',\n", + " 'abstain',\n", + " 'absurd',\n", + " 'abus',\n", + " 'abus new',\n", + " 'abus new york',\n", + " 'academi',\n", + " 'accept',\n", + " 'access',\n", + " 'access pipelin',\n", + " 'access pipelin protest']" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tfidf_v.get_feature_names()[:20]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'analyzer': 'word',\n", + " 'binary': False,\n", + " 'decode_error': 'strict',\n", + " 'dtype': numpy.int64,\n", + " 'encoding': 'utf-8',\n", + " 'input': 'content',\n", + " 'lowercase': True,\n", + " 'max_df': 1.0,\n", + " 'max_features': 5000,\n", + " 'min_df': 1,\n", + " 'ngram_range': (1, 3),\n", + " 'norm': 'l2',\n", + " 'preprocessor': None,\n", + " 'smooth_idf': True,\n", + " 'stop_words': None,\n", + " 'strip_accents': None,\n", + " 'sublinear_tf': False,\n", + " 'token_pattern': '(?u)\\\\b\\\\w\\\\w+\\\\b',\n", + " 'tokenizer': None,\n", + " 'use_idf': True,\n", + " 'vocabulary': None}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tfidf_v.get_params()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "count_df = pd.DataFrame(X_train, columns=tfidf_v.get_feature_names())" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_confusion_matrix(cm, classes,\n", + " normalize=False,\n", + " title='Confusion matrix',\n", + " cmap=plt.cm.Blues):\n", + " \"\"\"\n", + " See full source and example: \n", + " http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n", + " \n", + " This function prints and plots the confusion matrix.\n", + " Normalization can be applied by setting `normalize=True`.\n", + " \"\"\"\n", + " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", + " plt.title(title)\n", + " plt.colorbar()\n", + " tick_marks = np.arange(len(classes))\n", + " plt.xticks(tick_marks, classes, rotation=45)\n", + " plt.yticks(tick_marks, classes)\n", + "\n", + " if normalize:\n", + " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", + " print(\"Normalized confusion matrix\")\n", + " else:\n", + " print('Confusion matrix, without normalization')\n", + "\n", + " thresh = cm.max() / 2.\n", + " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", + " plt.text(j, i, cm[i, j],\n", + " horizontalalignment=\"center\",\n", + " color=\"white\" if cm[i, j] > thresh else \"black\")\n", + "\n", + " plt.tight_layout()\n", + " plt.ylabel('True label')\n", + " plt.xlabel('Predicted label')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MultinomialNB Algorithm" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "classifier=MultinomialNB()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import metrics\n", + "import numpy as np\n", + "import itertools" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy: 0.881\n", + "Confusion matrix, without normalization\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "classifier.fit(X_train, y_train)\n", + "pred = classifier.predict(X_test)\n", + "score = metrics.accuracy_score(y_test, pred)\n", + "print(\"accuracy: %0.3f\" % score)\n", + "cm = metrics.confusion_matrix(y_test, pred)\n", + "plot_confusion_matrix(cm, classes=['FAKE', 'REAL'])" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8810273405136703" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classifier.fit(X_train, y_train)\n", + "pred = classifier.predict(X_test)\n", + "score = metrics.accuracy_score(y_test, pred)\n", + "score" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(12250,)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_train.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Passive Aggressive Classifier Algorithm" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import PassiveAggressiveClassifier\n", + "linear_clf = PassiveAggressiveClassifier(n_iter=50)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\krish.naik\\appdata\\local\\continuum\\anaconda3\\envs\\nlp\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:117: DeprecationWarning: n_iter parameter is deprecated in 0.19 and will be removed in 0.21. Use max_iter and tol instead.\n", + " DeprecationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy: 0.918\n", + "Confusion matrix, without normalization\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "linear_clf.fit(X_train, y_train)\n", + "pred = linear_clf.predict(X_test)\n", + "score = metrics.accuracy_score(y_test, pred)\n", + "print(\"accuracy: %0.3f\" % score)\n", + "cm = metrics.confusion_matrix(y_test, pred)\n", + "plot_confusion_matrix(cm, classes=['FAKE Data', 'REAL Data'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multinomial Classifier with Hyperparameter" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "classifier=MultinomialNB(alpha=0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\krish.naik\\appdata\\local\\continuum\\anaconda3\\envs\\nlp\\lib\\site-packages\\sklearn\\naive_bayes.py:472: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n", + " 'setting alpha = %.1e' % _ALPHA_MIN)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Alpha: 0.0, Score : 0.8662800331400166\n", + "Alpha: 0.1, Score : 0.8777133388566695\n", + "Alpha: 0.2, Score : 0.8801988400994201\n", + "Alpha: 0.30000000000000004, Score : 0.87986743993372\n", + "Alpha: 0.4, Score : 0.8808616404308203\n", + "Alpha: 0.5, Score : 0.8806959403479702\n", + "Alpha: 0.6000000000000001, Score : 0.8815244407622204\n", + "Alpha: 0.7000000000000001, Score : 0.8813587406793704\n", + "Alpha: 0.8, Score : 0.8816901408450705\n", + "Alpha: 0.9, Score : 0.8816901408450705\n" + ] + } + ], + "source": [ + "previous_score=0\n", + "for alpha in np.arange(0,1,0.1):\n", + " sub_classifier=MultinomialNB(alpha=alpha)\n", + " sub_classifier.fit(X_train,y_train)\n", + " y_pred=sub_classifier.predict(X_test)\n", + " score = metrics.accuracy_score(y_test, y_pred)\n", + " if score>previous_score:\n", + " classifier=sub_classifier\n", + " print(\"Alpha: {}, Score : {}\".format(alpha,score))" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [], + "source": [ + "## Get Features names\n", + "feature_names = cv.get_feature_names()" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ -9.10038883, -8.62276128, -9.10038883, ..., -10.79498456,\n", + " -8.91467169, -9.32864749])" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classifier.coef_[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(-4.000149156604985, 'trump'),\n", + " (-4.287872694443541, 'hillari'),\n", + " (-4.396389621061519, 'clinton'),\n", + " (-4.899969726208735, 'elect'),\n", + " (-5.176598600897756, 'new'),\n", + " (-5.234730366348767, 'comment'),\n", + " (-5.273968180973631, 'video'),\n", + " (-5.3868167681180115, 'war'),\n", + " (-5.396821854078974, 'us'),\n", + " (-5.412019714988405, 'hillari clinton'),\n", + " (-5.417137433425386, 'fbi'),\n", + " (-5.48068448454208, 'vote'),\n", + " (-5.566255475855405, 'email'),\n", + " (-5.578238842742501, 'world'),\n", + " (-5.634015380199913, 'obama'),\n", + " (-5.734501455772904, 'donald'),\n", + " (-5.763095255139644, 'donald trump'),\n", + " (-5.785090276725191, 'russia'),\n", + " (-5.846224665218559, 'day'),\n", + " (-5.862110622807369, 'america')]" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### Most real\n", + "sorted(zip(classifier.coef_[0], feature_names), reverse=True)[:20]" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(-10.794984555596727, 'abe'),\n", + " (-10.794984555596727, 'abroad'),\n", + " (-10.794984555596727, 'abus new'),\n", + " (-10.794984555596727, 'abus new york'),\n", + " (-10.794984555596727, 'act new'),\n", + " (-10.794984555596727, 'act new york'),\n", + " (-10.794984555596727, 'advic'),\n", + " (-10.794984555596727, 'advis new'),\n", + " (-10.794984555596727, 'advis new york'),\n", + " (-10.794984555596727, 'age new'),\n", + " (-10.794984555596727, 'age new york'),\n", + " (-10.794984555596727, 'agenda breitbart'),\n", + " (-10.794984555596727, 'ail'),\n", + " (-10.794984555596727, 'aleppo new'),\n", + " (-10.794984555596727, 'aleppo new york'),\n", + " (-10.794984555596727, 'ali'),\n", + " (-10.794984555596727, 'america breitbart'),\n", + " (-10.794984555596727, 'america new york'),\n", + " (-10.794984555596727, 'american breitbart'),\n", + " (-10.794984555596727, 'american new'),\n", + " (-10.794984555596727, 'american new york'),\n", + " (-10.794984555596727, 'ami'),\n", + " (-10.794984555596727, 'ami schumer'),\n", + " (-10.794984555596727, 'amp'),\n", + " (-10.794984555596727, 'ann'),\n", + " (-10.794984555596727, 'ann coulter'),\n", + " (-10.794984555596727, 'applaud'),\n", + " (-10.794984555596727, 'arson'),\n", + " (-10.794984555596727, 'asian'),\n", + " (-10.794984555596727, 'assail'),\n", + " (-10.794984555596727, 'athlet'),\n", + " (-10.794984555596727, 'attack breitbart'),\n", + " (-10.794984555596727, 'attack new'),\n", + " (-10.794984555596727, 'attack new york'),\n", + " (-10.794984555596727, 'attack syria'),\n", + " (-10.794984555596727, 'attend'),\n", + " (-10.794984555596727, 'avert'),\n", + " (-10.794984555596727, 'away new'),\n", + " (-10.794984555596727, 'away new york'),\n", + " (-10.794984555596727, 'back new'),\n", + " (-10.794984555596727, 'back new york'),\n", + " (-10.794984555596727, 'baltimor'),\n", + " (-10.794984555596727, 'ban breitbart'),\n", + " (-10.794984555596727, 'ban new'),\n", + " (-10.794984555596727, 'ban new york'),\n", + " (-10.794984555596727, 'bangladesh'),\n", + " (-10.794984555596727, 'barron trump'),\n", + " (-10.794984555596727, 'basic'),\n", + " (-10.794984555596727, 'basketbal'),\n", + " (-10.794984555596727, 'basketbal player'),\n", + " (-10.794984555596727, 'baton'),\n", + " (-10.794984555596727, 'baton roug'),\n", + " (-10.794984555596727, 'battl new'),\n", + " (-10.794984555596727, 'battl new york'),\n", + " (-10.794984555596727, 'bay'),\n", + " (-10.794984555596727, 'beach new'),\n", + " (-10.794984555596727, 'beach new york'),\n", + " (-10.794984555596727, 'begin new'),\n", + " (-10.794984555596727, 'behead photo'),\n", + " (-10.794984555596727, 'behind trump'),\n", + " (-10.794984555596727, 'berkeley breitbart'),\n", + " (-10.794984555596727, 'betsi'),\n", + " (-10.794984555596727, 'betsi devo'),\n", + " (-10.794984555596727, 'bill breitbart'),\n", + " (-10.794984555596727, 'bill new'),\n", + " (-10.794984555596727, 'bill new york'),\n", + " (-10.794984555596727, 'bill reilli'),\n", + " (-10.794984555596727, 'billi'),\n", + " (-10.794984555596727, 'blue state'),\n", + " (-10.794984555596727, 'blue state blue'),\n", + " (-10.794984555596727, 'bolster'),\n", + " (-10.794984555596727, 'border patrol'),\n", + " (-10.794984555596727, 'border patrol agent'),\n", + " (-10.794984555596727, 'border state'),\n", + " (-10.794984555596727, 'border wall'),\n", + " (-10.794984555596727, 'border wall breitbart'),\n", + " (-10.794984555596727, 'bowl'),\n", + " (-10.794984555596727, 'box offic'),\n", + " (-10.794984555596727, 'bradi'),\n", + " (-10.794984555596727, 'breath'),\n", + " (-10.794984555596727, 'breitbart news daili'),\n", + " (-10.794984555596727, 'brief new'),\n", + " (-10.794984555596727, 'brief new york'),\n", + " (-10.794984555596727, 'broadway'),\n", + " (-10.794984555596727, 'bronx'),\n", + " (-10.794984555596727, 'brooklyn'),\n", + " (-10.794984555596727, 'brooklyn new'),\n", + " (-10.794984555596727, 'brooklyn new york'),\n", + " (-10.794984555596727, 'bureaucrat'),\n", + " (-10.794984555596727, 'caitlyn'),\n", + " (-10.794984555596727, 'caitlyn jenner'),\n", + " (-10.794984555596727, 'cal'),\n", + " (-10.794984555596727, 'california today'),\n", + " (-10.794984555596727, 'calm'),\n", + " (-10.794984555596727, 'campaign breitbart'),\n", + " (-10.794984555596727, 'campaign new'),\n", + " (-10.794984555596727, 'campaign new york'),\n", + " (-10.794984555596727, 'campu breitbart'),\n", + " (-10.794984555596727, 'cann'),\n", + " (-10.794984555596727, 'car new'),\n", + " (-10.794984555596727, 'car new york'),\n", + " (-10.794984555596727, 'care act'),\n", + " (-10.794984555596727, 'care act new'),\n", + " (-10.794984555596727, 'care bill'),\n", + " (-10.794984555596727, 'care breitbart'),\n", + " (-10.794984555596727, 'carri fisher'),\n", + " (-10.794984555596727, 'carson'),\n", + " (-10.794984555596727, 'case new'),\n", + " (-10.794984555596727, 'case new york'),\n", + " (-10.794984555596727, 'castro'),\n", + " (-10.794984555596727, 'caucu'),\n", + " (-10.794984555596727, 'ceas'),\n", + " (-10.794984555596727, 'ceas fire'),\n", + " (-10.794984555596727, 'chamber'),\n", + " (-10.794984555596727, 'championship'),\n", + " (-10.794984555596727, 'championship new'),\n", + " (-10.794984555596727, 'championship new york'),\n", + " (-10.794984555596727, 'chang new'),\n", + " (-10.794984555596727, 'chang new york'),\n", + " (-10.794984555596727, 'charg new york'),\n", + " (-10.794984555596727, 'charleston'),\n", + " (-10.794984555596727, 'charlott'),\n", + " (-10.794984555596727, 'cher'),\n", + " (-10.794984555596727, 'chief new'),\n", + " (-10.794984555596727, 'chief new york'),\n", + " (-10.794984555596727, 'china new york'),\n", + " (-10.794984555596727, 'chri christi'),\n", + " (-10.794984555596727, 'christi'),\n", + " (-10.794984555596727, 'christoph'),\n", + " (-10.794984555596727, 'chuck'),\n", + " (-10.794984555596727, 'chuck todd'),\n", + " (-10.794984555596727, 'circuit'),\n", + " (-10.794984555596727, 'citi new'),\n", + " (-10.794984555596727, 'citi new york'),\n", + " (-10.794984555596727, 'citizenship'),\n", + " (-10.794984555596727, 'clinton new york'),\n", + " (-10.794984555596727, 'clue'),\n", + " (-10.794984555596727, 'coach'),\n", + " (-10.794984555596727, 'colleg new'),\n", + " (-10.794984555596727, 'colleg new york'),\n", + " (-10.794984555596727, 'collid'),\n", + " (-10.794984555596727, 'collin'),\n", + " (-10.794984555596727, 'comedi'),\n", + " (-10.794984555596727, 'comedi central'),\n", + " (-10.794984555596727, 'comey fire'),\n", + " (-10.794984555596727, 'commission'),\n", + " (-10.794984555596727, 'common core'),\n", + " (-10.794984555596727, 'common ground'),\n", + " (-10.794984555596727, 'compet'),\n", + " (-10.794984555596727, 'complex'),\n", + " (-10.794984555596727, 'conceal'),\n", + " (-10.794984555596727, 'confirm hear'),\n", + " (-10.794984555596727, 'congress breitbart'),\n", + " (-10.794984555596727, 'congress new york'),\n", + " (-10.794984555596727, 'control new'),\n", + " (-10.794984555596727, 'control new york'),\n", + " (-10.794984555596727, 'convent new'),\n", + " (-10.794984555596727, 'convent new york'),\n", + " (-10.794984555596727, 'corner'),\n", + " (-10.794984555596727, 'coulter'),\n", + " (-10.794984555596727, 'counsel'),\n", + " (-10.794984555596727, 'countri breitbart'),\n", + " (-10.794984555596727, 'cours'),\n", + " (-10.794984555596727, 'court block'),\n", + " (-10.794984555596727, 'court new'),\n", + " (-10.794984555596727, 'court new york'),\n", + " (-10.794984555596727, 'court rule new'),\n", + " (-10.794984555596727, 'cpac'),\n", + " (-10.794984555596727, 'crash new'),\n", + " (-10.794984555596727, 'crash new york'),\n", + " (-10.794984555596727, 'crime breitbart'),\n", + " (-10.794984555596727, 'crisi new'),\n", + " (-10.794984555596727, 'crisi new york'),\n", + " (-10.794984555596727, 'critic new'),\n", + " (-10.794984555596727, 'crown'),\n", + " (-10.794984555596727, 'cunningham'),\n", + " (-10.794984555596727, 'curb'),\n", + " (-10.794984555596727, 'davo'),\n", + " (-10.794984555596727, 'day breitbart'),\n", + " (-10.794984555596727, 'day new york'),\n", + " (-10.794984555596727, 'day parad'),\n", + " (-10.794984555596727, 'dead new'),\n", + " (-10.794984555596727, 'dead new york'),\n", + " (-10.794984555596727, 'deal breitbart'),\n", + " (-10.794984555596727, 'deal new'),\n", + " (-10.794984555596727, 'deal new york'),\n", + " (-10.794984555596727, 'death new'),\n", + " (-10.794984555596727, 'death new york'),\n", + " (-10.794984555596727, 'debat new'),\n", + " (-10.794984555596727, 'debat new york'),\n", + " (-10.794984555596727, 'debbi reynold'),\n", + " (-10.794984555596727, 'defi'),\n", + " (-10.794984555596727, 'defund'),\n", + " (-10.794984555596727, 'delingpol'),\n", + " (-10.794984555596727, 'dem rep'),\n", + " (-10.794984555596727, 'dem sen'),\n", + " (-10.794984555596727, 'democrat breitbart'),\n", + " (-10.794984555596727, 'democrat new'),\n", + " (-10.794984555596727, 'democrat new york'),\n", + " (-10.794984555596727, 'deport breitbart'),\n", + " (-10.794984555596727, 'despair'),\n", + " (-10.794984555596727, 'devo'),\n", + " (-10.794984555596727, 'dh'),\n", + " (-10.794984555596727, 'die new'),\n", + " (-10.794984555596727, 'die new york'),\n", + " (-10.794984555596727, 'diego'),\n", + " (-10.794984555596727, 'disappoint'),\n", + " (-10.794984555596727, 'divorc'),\n", + " (-10.794984555596727, 'donald trump breitbart'),\n", + " (-10.794984555596727, 'donald trump campaign'),\n", + " (-10.794984555596727, 'donald trump inaugur'),\n", + " (-10.794984555596727, 'dossier'),\n", + " (-10.794984555596727, 'drama'),\n", + " (-10.794984555596727, 'drive car'),\n", + " (-10.794984555596727, 'drug new'),\n", + " (-10.794984555596727, 'drug new york'),\n", + " (-10.794984555596727, 'dunham'),\n", + " (-10.794984555596727, 'dylann'),\n", + " (-10.794984555596727, 'dylann roof'),\n", + " (-10.794984555596727, 'eas'),\n", + " (-10.794984555596727, 'easter'),\n", + " (-10.794984555596727, 'economi new'),\n", + " 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new york'),\n", + " (-10.794984555596727, 'wrestl'),\n", + " (-10.794984555596727, 'yahoo'),\n", + " (-10.794984555596727, 'year breitbart'),\n", + " (-10.794984555596727, 'year eve'),\n", + " (-10.794984555596727, 'year new york'),\n", + " (-10.794984555596727, 'york new'),\n", + " (-10.794984555596727, 'york new york'),\n", + " (-10.794984555596727, 'york state'),\n", + " (-10.794984555596727, 'zone new'),\n", + " (-10.794984555596727, 'zone new york'),\n", + " (-10.794984555596727, 'zoo'),\n", + " (-10.047770153766505, 'academi'),\n", + " (-10.047770153766505, 'accid'),\n", + " (-10.047770153766505, 'aclu'),\n", + " (-10.047770153766505, 'acr'),\n", + " (-10.047770153766505, 'adopt'),\n", + " (-10.047770153766505, 'ahead trump'),\n", + " (-10.047770153766505, 'airbnb'),\n", + " (-10.047770153766505, 'airlin'),\n", + " (-10.047770153766505, 'album'),\n", + " (-10.047770153766505, 'alex jone'),\n", + " (-10.047770153766505, 'allegedli'),\n", + " (-10.047770153766505, 'allianc'),\n", + " (-10.047770153766505, 'america new'),\n", + " (-10.047770153766505, 'anchor'),\n", + " (-10.047770153766505, 'anger'),\n", + " (-10.047770153766505, 'anti israel'),\n", + " (-10.047770153766505, 'anti semit'),\n", + " (-10.047770153766505, 'anymor'),\n", + " (-10.047770153766505, 'applic'),\n", + " (-10.047770153766505, 'approach'),\n", + " (-10.047770153766505, 'appropri'),\n", + " (-10.047770153766505, 'around world'),\n", + " (-10.047770153766505, 'artifici'),\n", + " (-10.047770153766505, 'asylum seeker'),\n", + " (-10.047770153766505, 'atlant'),\n", + " (-10.047770153766505, 'attack polic'),\n", + " (-10.047770153766505, 'attack trump'),\n", + " (-10.047770153766505, 'auto'),\n", + " (-10.047770153766505, 'aviv'),\n", + " (-10.047770153766505, 'back trump'),\n", + " (-10.047770153766505, 'baghdad'),\n", + " (-10.047770153766505, 'band'),\n", + " (-10.047770153766505, 'barrier'),\n", + " (-10.047770153766505, 'barron'),\n", + " (-10.047770153766505, 'beach'),\n", + " (-10.047770153766505, 'beer'),\n", + " (-10.047770153766505, 'behead'),\n", + " (-10.047770153766505, 'beij'),\n", + " (-10.047770153766505, 'ben'),\n", + " (-10.047770153766505, 'berlin'),\n", + " (-10.047770153766505, 'bet'),\n", + " (-10.047770153766505, 'beyonc'),\n", + " (-10.047770153766505, 'bill cosbi'),\n", + " (-10.047770153766505, 'bitter'),\n", + " (-10.047770153766505, 'blasio'),\n", + " (-10.047770153766505, 'block trump'),\n", + " (-10.047770153766505, 'boat'),\n", + " (-10.047770153766505, 'boe'),\n", + " (-10.047770153766505, 'boko'),\n", + " (-10.047770153766505, 'boko haram'),\n", + " (-10.047770153766505, 'bolton'),\n", + " (-10.047770153766505, 'bomber'),\n", + " (-10.047770153766505, 'bori'),\n", + " (-10.047770153766505, 'boss'),\n", + " (-10.047770153766505, 'bow'),\n", + " (-10.047770153766505, 'branch'),\n", + " (-10.047770153766505, 'breitbart news'),\n", + " (-10.047770153766505, 'bribe'),\n", + " (-10.047770153766505, 'brought'),\n", + " (-10.047770153766505, 'built'),\n", + " (-10.047770153766505, 'bull'),\n", + " ...]" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### Most fake\n", + "sorted(zip(classifier.coef_[0], feature_names))[:5000]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## HashingVectorizer\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\krish.naik\\appdata\\local\\continuum\\anaconda3\\envs\\nlp\\lib\\site-packages\\sklearn\\feature_extraction\\hashing.py:94: DeprecationWarning: the option non_negative=True has been deprecated in 0.19 and will be removed in version 0.21.\n", + " \" in version 0.21.\", DeprecationWarning)\n", + "c:\\users\\krish.naik\\appdata\\local\\continuum\\anaconda3\\envs\\nlp\\lib\\site-packages\\sklearn\\feature_extraction\\hashing.py:94: DeprecationWarning: the option non_negative=True has been deprecated in 0.19 and will be removed in version 0.21.\n", + " \" in version 0.21.\", DeprecationWarning)\n" + ] + } + ], + "source": [ + "hs_vectorizer=HashingVectorizer(n_features=5000,non_negative=True)\n", + "X=hs_vectorizer.fit_transform(corpus).toarray()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18285, 5000)" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0. , 0.31622777, 0. , ..., 0. , 0. ,\n", + " 0. ],\n", + " [0. , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ],\n", + " [0. , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ],\n", + " ...,\n", + " [0. , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ],\n", + " [0. , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ],\n", + " [0. , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ]])" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "## Divide the dataset into Train and Test\n", + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy: 0.871\n", + "Confusion matrix, without normalization\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "classifier=MultinomialNB()\n", + "classifier.fit(X_train, y_train)\n", + "pred = classifier.predict(X_test)\n", + "score = metrics.accuracy_score(y_test, pred)\n", + "print(\"accuracy: %0.3f\" % score)\n", + "cm = metrics.confusion_matrix(y_test, pred)\n", + "plot_confusion_matrix(cm, classes=['FAKE', 'REAL'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "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": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}