Fake News Classifier Dataset: https://www.kaggle.com/c/fake-news/data# import pandas as pd df=pd.read_csv('fake-news/train.csv') df.head() id title author text label 0 0 House Dem Aide: We Didn’t Even See Comey’s Let... Darrell Lucus House Dem Aide: We Didn’t Even See Comey’s Let... 1 1 1 FLYNN: Hillary Clinton, Big Woman on Campus - ... Daniel J. Flynn Ever get the feeling your life circles the rou... 0 2 2 Why the Truth Might Get You Fired Consortiumnews.com Why the Truth Might Get You Fired October 29, ... 1 3 3 15 Civilians Killed In Single US Airstrike Hav... Jessica Purkiss Videos 15 Civilians Killed In Single US Airstr... 1 4 4 Iranian woman jailed for fictional unpublished... Howard Portnoy Print \nAn Iranian woman has been sentenced to... 1 ## Get the Independent Features X=df.drop('label',axis=1) X.head() id title author text 0 0 House Dem Aide: We Didn’t Even See Comey’s Let... Darrell Lucus House Dem Aide: We Didn’t Even See Comey’s Let... 1 1 FLYNN: Hillary Clinton, Big Woman on Campus - ... Daniel J. Flynn Ever get the feeling your life circles the rou... 2 2 Why the Truth Might Get You Fired Consortiumnews.com Why the Truth Might Get You Fired October 29, ... 3 3 15 Civilians Killed In Single US Airstrike Hav... Jessica Purkiss Videos 15 Civilians Killed In Single US Airstr... 4 4 Iranian woman jailed for fictional unpublished... Howard Portnoy Print \nAn Iranian woman has been sentenced to... ## Get the Dependent features y=df['label'] y.head() 0 1 1 0 2 1 3 1 4 1 Name: label, dtype: int64 df.shape (18285, 5) from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer df=df.dropna() df.head(10) id title author text label 0 0 House Dem Aide: We Didn’t Even See Comey’s Let... Darrell Lucus House Dem Aide: We Didn’t Even See Comey’s Let... 1 1 1 FLYNN: Hillary Clinton, Big Woman on Campus - ... Daniel J. Flynn Ever get the feeling your life circles the rou... 0 2 2 Why the Truth Might Get You Fired Consortiumnews.com Why the Truth Might Get You Fired October 29, ... 1 3 3 15 Civilians Killed In Single US Airstrike Hav... Jessica Purkiss Videos 15 Civilians Killed In Single US Airstr... 1 4 4 Iranian woman jailed for fictional unpublished... Howard Portnoy Print \nAn Iranian woman has been sentenced to... 1 5 5 Jackie Mason: Hollywood Would Love Trump if He... Daniel Nussbaum In these trying times, Jackie Mason is the Voi... 0 7 7 Benoît Hamon Wins French Socialist Party’s Pre... Alissa J. Rubin PARIS — France chose an idealistic, traditi... 0 9 9 A Back-Channel Plan for Ukraine and Russia, Co... Megan Twohey and Scott Shane A week before Michael T. Flynn resigned as nat... 0 10 10 Obama’s Organizing for Action Partners with So... Aaron Klein Organizing for Action, the activist group that... 0 11 11 BBC Comedy Sketch "Real Housewives of ISIS" Ca... Chris Tomlinson The BBC produced spoof on the “Real Housewives... 0 messages=df.copy() messages.reset_index(inplace=True) messages.head(10) index id title author text label 0 0 0 House Dem Aide: We Didn’t Even See Comey’s Let... Darrell Lucus House Dem Aide: We Didn’t Even See Comey’s Let... 1 1 1 1 FLYNN: Hillary Clinton, Big Woman on Campus - ... Daniel J. Flynn Ever get the feeling your life circles the rou... 0 2 2 2 Why the Truth Might Get You Fired Consortiumnews.com Why the Truth Might Get You Fired October 29, ... 1 3 3 3 15 Civilians Killed In Single US Airstrike Hav... Jessica Purkiss Videos 15 Civilians Killed In Single US Airstr... 1 4 4 4 Iranian woman jailed for fictional unpublished... Howard Portnoy Print \nAn Iranian woman has been sentenced to... 1 5 5 5 Jackie Mason: Hollywood Would Love Trump if He... Daniel Nussbaum In these trying times, Jackie Mason is the Voi... 0 6 7 7 Benoît Hamon Wins French Socialist Party’s Pre... Alissa J. Rubin PARIS — France chose an idealistic, traditi... 0 7 9 9 A Back-Channel Plan for Ukraine and Russia, Co... Megan Twohey and Scott Shane A week before Michael T. Flynn resigned as nat... 0 8 10 10 Obama’s Organizing for Action Partners with So... Aaron Klein Organizing for Action, the activist group that... 0 9 11 11 BBC Comedy Sketch "Real Housewives of ISIS" Ca... Chris Tomlinson The BBC produced spoof on the “Real Housewives... 0 messages['title'][6] 'Benoît Hamon Wins French Socialist Party’s Presidential Nomination - The New York Times' from nltk.corpus import stopwords from nltk.stem.porter import PorterStemmer ps = PorterStemmer() corpus = [] for i in range(0, len(messages)): review = re.sub('[^a-zA-Z]', ' ', messages['title'][i]) review = review.lower() review = review.split() review = [ps.stem(word) for word in review if not word in stopwords.words('english')] review = ' '.join(review) corpus.append(review) corpus[3] 'civilian kill singl us airstrik identifi' ## Applying Countvectorizer # Creating the Bag of Words model from sklearn.feature_extraction.text import CountVectorizer cv = CountVectorizer(max_features=5000,ngram_range=(1,3)) X = cv.fit_transform(corpus).toarray() X.shape (18285, 5000) y=messages['label'] ## Divide the dataset into Train and Test from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=0) cv.get_feature_names()[:20] ['abandon', 'abc', 'abc news', 'abduct', 'abe', 'abedin', 'abl', 'abort', 'abroad', 'absolut', 'abstain', 'absurd', 'abus', 'abus new', 'abus new york', 'academi', 'accept', 'access', 'access pipelin', 'access pipelin protest'] cv.get_params() {'analyzer': 'word', 'binary': False, 'decode_error': 'strict', 'dtype': numpy.int64, 'encoding': 'utf-8', 'input': 'content', 'lowercase': True, 'max_df': 1.0, 'max_features': 5000, 'min_df': 1, 'ngram_range': (1, 3), 'preprocessor': None, 'stop_words': None, 'strip_accents': None, 'token_pattern': '(?u)\\b\\w\\w+\\b', 'tokenizer': None, 'vocabulary': None} count_df = pd.DataFrame(X_train, columns=cv.get_feature_names()) count_df.head() abandon abc abc news abduct abe abedin abl abort abroad absolut ... zero zika zika viru zionist zone zone new zone new york zoo zu zuckerberg 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 1 ... 0 0 0 0 0 0 0 0 0 0 5 rows × 5000 columns import matplotlib.pyplot as plt def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues): """ See full source and example: http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. """ plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=45) plt.yticks(tick_marks, classes) if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] print("Normalized confusion matrix") else: print('Confusion matrix, without normalization') thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, cm[i, j], horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label') MultinomialNB Algorithm from sklearn.naive_bayes import MultinomialNB classifier=MultinomialNB() from sklearn import metrics import numpy as np import itertools classifier.fit(X_train, y_train) pred = classifier.predict(X_test) score = metrics.accuracy_score(y_test, pred) print("accuracy: %0.3f" % score) cm = metrics.confusion_matrix(y_test, pred) plot_confusion_matrix(cm, classes=['FAKE', 'REAL']) accuracy: 0.902 Confusion matrix, without normalization classifier.fit(X_train, y_train) pred = classifier.predict(X_test) score = metrics.accuracy_score(y_test, pred) score 0.9019055509527755 y_train.shape (12250,) Passive Aggressive Classifier Algorithm from sklearn.linear_model import PassiveAggressiveClassifier linear_clf = PassiveAggressiveClassifier(n_iter=50) linear_clf.fit(X_train, y_train) pred = linear_clf.predict(X_test) score = metrics.accuracy_score(y_test, pred) print("accuracy: %0.3f" % score) cm = metrics.confusion_matrix(y_test, pred) plot_confusion_matrix(cm, classes=['FAKE Data', 'REAL Data']) 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. DeprecationWarning) accuracy: 0.918 Confusion matrix, without normalization Multinomial Classifier with Hyperparameter classifier=MultinomialNB(alpha=0.1) previous_score=0 for alpha in np.arange(0,1,0.1): sub_classifier=MultinomialNB(alpha=alpha) sub_classifier.fit(X_train,y_train) y_pred=sub_classifier.predict(X_test) score = metrics.accuracy_score(y_test, y_pred) if score>previous_score: classifier=sub_classifier print("Alpha: {}, Score : {}".format(alpha,score)) Alpha: 0.0, Score : 0.8903065451532726 Alpha: 0.1, Score : 0.9020712510356255 Alpha: 0.2, Score : 0.9025683512841757 Alpha: 0.30000000000000004, Score : 0.9024026512013256 Alpha: 0.4, Score : 0.9017398508699255 Alpha: 0.5, Score : 0.9015741507870754 Alpha: 0.6000000000000001, Score : 0.9022369511184756 Alpha: 0.7000000000000001, Score : 0.9025683512841757 Alpha: 0.8, Score : 0.9015741507870754 Alpha: 0.9, Score : 0.9017398508699255 ## Get Features names feature_names = cv.get_feature_names() classifier.coef_[0] array([ -9.10038883, -8.62276128, -9.10038883, ..., -10.79498456, -8.91467169, -9.32864749]) ### Most real sorted(zip(classifier.coef_[0], feature_names), reverse=True)[:20] [(-4.000149156604985, 'trump'), (-4.287872694443541, 'hillari'), (-4.396389621061519, 'clinton'), (-4.899969726208735, 'elect'), (-5.176598600897756, 'new'), (-5.234730366348767, 'comment'), (-5.273968180973631, 'video'), (-5.3868167681180115, 'war'), (-5.396821854078974, 'us'), (-5.412019714988405, 'hillari clinton'), (-5.417137433425386, 'fbi'), (-5.48068448454208, 'vote'), (-5.566255475855405, 'email'), (-5.578238842742501, 'world'), (-5.634015380199913, 'obama'), (-5.734501455772904, 'donald'), (-5.763095255139644, 'donald trump'), (-5.785090276725191, 'russia'), (-5.846224665218559, 'day'), (-5.862110622807369, 'america')] ### Most fake sorted(zip(classifier.coef_[0], feature_names))[:5000] [(-10.794984555596727, 'abe'), (-10.794984555596727, 'abroad'), (-10.794984555596727, 'abus new'), (-10.794984555596727, 'abus new york'), (-10.794984555596727, 'act new'), (-10.794984555596727, 'act new york'), (-10.794984555596727, 'advic'), (-10.794984555596727, 'advis new'), (-10.794984555596727, 'advis new york'), (-10.794984555596727, 'age new'), (-10.794984555596727, 'age new york'), (-10.794984555596727, 'agenda breitbart'), (-10.794984555596727, 'ail'), (-10.794984555596727, 'aleppo new'), (-10.794984555596727, 'aleppo new york'), (-10.794984555596727, 'ali'), (-10.794984555596727, 'america breitbart'), (-10.794984555596727, 'america new york'), (-10.794984555596727, 'american breitbart'), (-10.794984555596727, 'american new'), (-10.794984555596727, 'american new york'), (-10.794984555596727, 'ami'), (-10.794984555596727, 'ami schumer'), (-10.794984555596727, 'amp'), (-10.794984555596727, 'ann'), (-10.794984555596727, 'ann coulter'), (-10.794984555596727, 'applaud'), (-10.794984555596727, 'arson'), (-10.794984555596727, 'asian'), (-10.794984555596727, 'assail'), (-10.794984555596727, 'athlet'), (-10.794984555596727, 'attack breitbart'), (-10.794984555596727, 'attack new'), (-10.794984555596727, 'attack new york'), (-10.794984555596727, 'attack syria'), (-10.794984555596727, 'attend'), (-10.794984555596727, 'avert'), (-10.794984555596727, 'away new'), (-10.794984555596727, 'away new york'), (-10.794984555596727, 'back new'), (-10.794984555596727, 'back new york'), (-10.794984555596727, 'baltimor'), (-10.794984555596727, 'ban breitbart'), (-10.794984555596727, 'ban new'), (-10.794984555596727, 'ban new york'), (-10.794984555596727, 'bangladesh'), (-10.794984555596727, 'barron trump'), (-10.794984555596727, 'basic'), (-10.794984555596727, 'basketbal'), (-10.794984555596727, 'basketbal player'), (-10.794984555596727, 'baton'), (-10.794984555596727, 'baton roug'), (-10.794984555596727, 'battl new'), (-10.794984555596727, 'battl new york'), (-10.794984555596727, 'bay'), (-10.794984555596727, 'beach new'), (-10.794984555596727, 'beach new york'), (-10.794984555596727, 'begin new'), (-10.794984555596727, 'behead photo'), (-10.794984555596727, 'behind trump'), (-10.794984555596727, 'berkeley breitbart'), (-10.794984555596727, 'betsi'), (-10.794984555596727, 'betsi devo'), (-10.794984555596727, 'bill breitbart'), (-10.794984555596727, 'bill new'), (-10.794984555596727, 'bill new york'), (-10.794984555596727, 'bill reilli'), (-10.794984555596727, 'billi'), (-10.794984555596727, 'blue state'), (-10.794984555596727, 'blue state blue'), (-10.794984555596727, 'bolster'), (-10.794984555596727, 'border patrol'), (-10.794984555596727, 'border patrol agent'), (-10.794984555596727, 'border state'), (-10.794984555596727, 'border wall'), (-10.794984555596727, 'border wall breitbart'), (-10.794984555596727, 'bowl'), (-10.794984555596727, 'box offic'), (-10.794984555596727, 'bradi'), (-10.794984555596727, 'breath'), (-10.794984555596727, 'breitbart news daili'), (-10.794984555596727, 'brief new'), (-10.794984555596727, 'brief new york'), (-10.794984555596727, 'broadway'), (-10.794984555596727, 'bronx'), (-10.794984555596727, 'brooklyn'), (-10.794984555596727, 'brooklyn new'), (-10.794984555596727, 'brooklyn new york'), (-10.794984555596727, 'bureaucrat'), (-10.794984555596727, 'caitlyn'), (-10.794984555596727, 'caitlyn jenner'), (-10.794984555596727, 'cal'), (-10.794984555596727, 'california today'), (-10.794984555596727, 'calm'), (-10.794984555596727, 'campaign breitbart'), (-10.794984555596727, 'campaign new'), (-10.794984555596727, 'campaign new york'), (-10.794984555596727, 'campu breitbart'), (-10.794984555596727, 'cann'), (-10.794984555596727, 'car new'), (-10.794984555596727, 'car new york'), (-10.794984555596727, 'care act'), (-10.794984555596727, 'care act new'), (-10.794984555596727, 'care bill'), (-10.794984555596727, 'care breitbart'), (-10.794984555596727, 'carri fisher'), (-10.794984555596727, 'carson'), (-10.794984555596727, 'case new'), (-10.794984555596727, 'case new york'), (-10.794984555596727, 'castro'), (-10.794984555596727, 'caucu'), (-10.794984555596727, 'ceas'), (-10.794984555596727, 'ceas fire'), (-10.794984555596727, 'chamber'), (-10.794984555596727, 'championship'), (-10.794984555596727, 'championship new'), (-10.794984555596727, 'championship new york'), (-10.794984555596727, 'chang new'), (-10.794984555596727, 'chang new york'), (-10.794984555596727, 'charg new york'), (-10.794984555596727, 'charleston'), (-10.794984555596727, 'charlott'), (-10.794984555596727, 'cher'), (-10.794984555596727, 'chief new'), (-10.794984555596727, 'chief new york'), (-10.794984555596727, 'china new york'), (-10.794984555596727, 'chri christi'), (-10.794984555596727, 'christi'), (-10.794984555596727, 'christoph'), (-10.794984555596727, 'chuck'), (-10.794984555596727, 'chuck todd'), (-10.794984555596727, 'circuit'), (-10.794984555596727, 'citi new'), (-10.794984555596727, 'citi new york'), (-10.794984555596727, 'citizenship'), (-10.794984555596727, 'clinton new york'), (-10.794984555596727, 'clue'), (-10.794984555596727, 'coach'), (-10.794984555596727, 'colleg new'), (-10.794984555596727, 'colleg new york'), (-10.794984555596727, 'collid'), (-10.794984555596727, 'collin'), (-10.794984555596727, 'comedi'), (-10.794984555596727, 'comedi central'), (-10.794984555596727, 'comey fire'), (-10.794984555596727, 'commission'), (-10.794984555596727, 'common core'), (-10.794984555596727, 'common ground'), (-10.794984555596727, 'compet'), (-10.794984555596727, 'complex'), (-10.794984555596727, 'conceal'), (-10.794984555596727, 'confirm hear'), (-10.794984555596727, 'congress breitbart'), (-10.794984555596727, 'congress new york'), (-10.794984555596727, 'control new'), (-10.794984555596727, 'control new york'), (-10.794984555596727, 'convent new'), (-10.794984555596727, 'convent new york'), (-10.794984555596727, 'corner'), (-10.794984555596727, 'coulter'), (-10.794984555596727, 'counsel'), (-10.794984555596727, 'countri breitbart'), (-10.794984555596727, 'cours'), (-10.794984555596727, 'court block'), (-10.794984555596727, 'court new'), (-10.794984555596727, 'court new york'), (-10.794984555596727, 'court rule new'), (-10.794984555596727, 'cpac'), (-10.794984555596727, 'crash new'), (-10.794984555596727, 'crash new york'), (-10.794984555596727, 'crime breitbart'), (-10.794984555596727, 'crisi new'), (-10.794984555596727, 'crisi new york'), (-10.794984555596727, 'critic new'), (-10.794984555596727, 'crown'), (-10.794984555596727, 'cunningham'), (-10.794984555596727, 'curb'), (-10.794984555596727, 'davo'), (-10.794984555596727, 'day breitbart'), (-10.794984555596727, 'day new york'), (-10.794984555596727, 'day parad'), (-10.794984555596727, 'dead new'), (-10.794984555596727, 'dead new york'), (-10.794984555596727, 'deal breitbart'), (-10.794984555596727, 'deal new'), (-10.794984555596727, 'deal new york'), (-10.794984555596727, 'death new'), (-10.794984555596727, 'death new york'), (-10.794984555596727, 'debat new'), (-10.794984555596727, 'debat new york'), (-10.794984555596727, 'debbi reynold'), (-10.794984555596727, 'defi'), (-10.794984555596727, 'defund'), (-10.794984555596727, 'delingpol'), (-10.794984555596727, 'dem rep'), (-10.794984555596727, 'dem sen'), (-10.794984555596727, 'democrat breitbart'), (-10.794984555596727, 'democrat new'), (-10.794984555596727, 'democrat new york'), (-10.794984555596727, 'deport breitbart'), (-10.794984555596727, 'despair'), (-10.794984555596727, 'devo'), (-10.794984555596727, 'dh'), (-10.794984555596727, 'die new'), (-10.794984555596727, 'die new york'), (-10.794984555596727, 'diego'), (-10.794984555596727, 'disappoint'), (-10.794984555596727, 'divorc'), (-10.794984555596727, 'donald trump breitbart'), (-10.794984555596727, 'donald trump campaign'), (-10.794984555596727, 'donald trump inaugur'), (-10.794984555596727, 'dossier'), (-10.794984555596727, 'drama'), (-10.794984555596727, 'drive car'), (-10.794984555596727, 'drug new'), (-10.794984555596727, 'drug new york'), (-10.794984555596727, 'dunham'), (-10.794984555596727, 'dylann'), (-10.794984555596727, 'dylann roof'), (-10.794984555596727, 'eas'), (-10.794984555596727, 'easter'), (-10.794984555596727, 'economi new'), (-10.794984555596727, 'ecuador'), (-10.794984555596727, 'effort new york'), (-10.794984555596727, 'egyptair'), (-10.794984555596727, 'elect breitbart'), (-10.794984555596727, 'elect loss'), (-10.794984555596727, 'elect new york'), (-10.794984555596727, 'els new'), (-10.794984555596727, 'els new york'), (-10.794984555596727, 'embassi jerusalem'), (-10.794984555596727, 'emmi'), (-10.794984555596727, 'end new'), (-10.794984555596727, 'end new york'), (-10.794984555596727, 'england'), (-10.794984555596727, 'england patriot'), (-10.794984555596727, 'epa'), (-10.794984555596727, 'era new'), (-10.794984555596727, 'era new york'), (-10.794984555596727, 'espn'), (-10.794984555596727, 'europ new york'), (-10.794984555596727, 'european union'), (-10.794984555596727, 'even brief'), (-10.794984555596727, 'even brief new'), 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