def main():
global tagger
if constants.corpus == constants.Corpus.movie_review:
neg_docs, pos_docs = get_movie_corpus()
if constants.corpus == constants.Corpus.pol_debates:
neg_docs, pos_docs = get_political_debates()
if constants.mark_negation:
neg_docs = [nltk.sentiment.util.mark_negation(doc) for doc in neg_docs]
pos_docs = [nltk.sentiment.util.mark_negation(doc) for doc in pos_docs]
# Split betweeen the training set and the testing set
num_train_neg = int(3 / 4 * len(neg_docs))
num_test_neg = len(neg_docs) - num_train_neg
num_train_pos = int(3 / 4 * len(pos_docs))
num_test_pos = len(pos_docs) - num_train_pos
train_neg, test_neg = sklearn.cross_validation.train_test_split(
neg_docs, train_size=num_train_neg, test_size=num_test_neg)
train_pos, test_pos = sklearn.cross_validation.train_test_split(
pos_docs, train_size=num_train_pos, test_size=num_test_pos)
# Make the final train set and test set
train_docs = train_pos + train_neg
test_docs = test_pos + test_neg
# Set up the Sentiment Analyzer
analyzer = SentimentAnalyzer()
if constants.feature_extractor == constants.FeatureExtractor.bag_of_words:
analyzer.add_feat_extractor(extract_bag_of_words_feats)
if constants.feature_extractor == constants.FeatureExtractor.freq_dist:
analyzer.add_feat_extractor(extract_freq_dist)
elif constants.feature_extractor == constants.FeatureExtractor.unigram:
all_words = analyzer.all_words(train_docs, labeled=True)
unigram_features = analyzer.unigram_word_feats(all_words,
min_freq=1000)
print("Length of unigram features: %d" % len(unigram_features))
analyzer.add_feat_extractor(nltk.sentiment.util.extract_unigram_feats,
unigrams=unigram_features)
elif constants.feature_extractor == constants.FeatureExtractor.bigram_bag_of_words:
analyzer.add_feat_extractor(extract_sig_bigram_feats)
elif constants.feature_extractor == constants.FeatureExtractor.adjective_bag_of_words:
tagger = nltk.tag.HunposTagger(constants.hunpos_english_model)
analyzer.add_feat_extractor(adjective_bag_of_words)
elif constants.feature_extractor == constants.FeatureExtractor.pos_bag_of_words:
tagger = nltk.tag.HunposTagger(constants.hunpos_english_model)
analyzer.add_feat_extractor(adjective_bag_of_words)
train_feat = list(analyzer.apply_features(train_docs, labeled=True))
test_feat = list(analyzer.apply_features(test_docs, labeled=True))
print('train on %d instances, test on %d instances' %
(len(train_feat), len(test_feat)))
if constants.classifier == constants.Classifier.naive_bays:
classifier = NaiveBayesClassifier.train(train_feat)
analyzer.evaluate(test_feat,
classifier,
accuracy=True,
f_measure=True,
precision=True,
recall=True,
verbose=True)
classifier.show_most_informative_features()
# elif constants.classifier == constants.Classifier.maxent:
# classifier = MaxentClassifier.train(train_feat)
# analyzer.evaluate(test_feat, classifier, accuracy=True, f_measure=True, precision=True, recall=True,
# verbose=True)
# classifier.show_most_informative_features()
elif constants.classifier == constants.Classifier.decision_tree:
classifier = SklearnClassifier(
DecisionTreeClassifier()).train(train_feat)
analyzer.evaluate(test_feat,
classifier,
accuracy=True,
f_measure=True,
precision=True,
recall=True,
verbose=True)
elif constants.classifier == constants.Classifier.linear_svm:
classifier = SklearnClassifier(LinearSVC()).train(train_feat)
analyzer.evaluate(test_feat,
classifier,
accuracy=True,
f_measure=True,
precision=True,
recall=True,
verbose=True)
elif constants.classifier == constants.Classifier.random_forest:
classifier = SklearnClassifier(
RandomForestClassifier()).train(train_feat)
analyzer.evaluate(test_feat,
classifier,
accuracy=True,
f_measure=True,
precision=True,
recall=True,
verbose=True)
elif constants.classifier == constants.Classifier.logistic:
classifier = SklearnClassifier(LogisticRegression()).train(train_feat)
analyzer.evaluate(test_feat,
classifier,
accuracy=True,
f_measure=True,
precision=True,
recall=True,
verbose=True)
#print(len(X_test))
if not preprocess_flag:
bow_transformer = joblib.load('FeatTransformer.pkl')
X_train = joblib.load('TrainFeatures.pkl')
X_test = joblib.load('TestFeatures.pkl')
else:
bow_transformer = CountVectorizer(analyzer=format_sentence).fit(X_train)
X_train = bow_transformer.transform(X_train)
X_test = bow_transformer.transform(X_test)
joblib.dump(bow_transformer, 'FeatTransformer.pkl')
joblib.dump(X_train, 'TrainFeatures.pkl')
joblib.dump(X_test, 'TestFeatures.pkl')
#train decision tree classifier
dt_flag = 0 #if 1, train model from scratch and dump - if 0, load dumped model
dt = DecisionTreeClassifier()
if dt_flag:
dt_clf = dt.fit(X_train, Y_train)
joblib.dump(dt_clf, 'DTmodel.pkl')
else:
dt_clf = joblib.load('DTmodel.pkl')
#test dt classifier
preds = dt_clf.predict(X_test)
cm = confusion_matrix(Y_test, preds)
print(cm)
print('\n')
print(classification_report(Y_test, preds))
#plot_roc_curve(dt_clf,X_test,Y_test)
plt.figure()
plot_confusion_matrix(cm, classes=['negative', 'positive'], normalize=True, title='Normalized confusion matrix - Decision Tree')
plt.show()
