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- import nltk
- import pickle
- from nltk.classify.scikitlearn import SklearnClassifier
- def pickling(file, document_name):
- save_documemts = open('../pickled_algos/' + document_name + '.pickle', 'wb')
- pickle.dump(file, save_documemts)
- save_documemts.close()
- # let the training_set contains data in the form of -
- # (sentence, sentiment), e.g.
- # This is a nice tea, 1
- # This is a bad tea, 0
- training_set
- # Original Naive Bayes
- classifier = nltk.NaiveBayesClassifier.train(training_set)
- pickling(classifier, 'original_naive_bayes_classifer')
- # Multinomial Naive Bayes
- MNB_classifier = SklearnClassifier(MultinomialNB())
- MNB_classifier.train(training_set)
- pickling(MNB_classifier, 'MNB_classifier')
- # Bernouli Naive Bayes
- Bernoulli_classifier = SklearnClassifier(BernoulliNB())
- Bernoulli_classifier.train(training_set)
- pickling(Bernoulli_classifier, 'Bernoulli_classifier')
- # Logistic Regression Classifier
- LogisticRegression_classifier = SklearnClassifier(LogisticRegression())
- LogisticRegression_classifier.train(training_set)
- pickling(LogisticRegression_classifier, 'LogisticRegression_classifier')
- # Linear SVC Classifer
- LinearSVC_classifier = SklearnClassifier(LinearSVC())
- LinearSVC_classifier.train(training_set)
- pickling(LinearSVC_classifier, 'LinearSVC_classifier')
- # NuSVC Classigier
- NuSVC_classifier = SklearnClassifier(NuSVC())
- NuSVC_classifier.train(training_set)
- pickling(NuSVC_classifier, 'NuSVC_classifier')
- # SGDC Classifier
- SGDC_classifier = SklearnClassifier(SGDClassifier())
- SGDC_classifier.train(training_set)
- pickling(SGDC_classifier, 'SGDC_classifier')
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