class InterestAnalyzer:
def __init__(self):
self.feature_builder = FeatureBuilder()
def rebuild_features(self):
print 'Parsing Facebook...'
fbparser.parse({'in': TRAINING_DIRECTORIES['fb'], 'out': TRAINING_FILES['fb']})
print 'Parsing Twitter...'
tweetparser.parse({'in': TRAINING_DIRECTORIES['tweets'], 'out': TRAINING_FILES['tweets']})
print 'Parsing LinkedIn...'
linkedinparser.parse({'in': TRAINING_DIRECTORIES['linkedin'], 'out': TRAINING_FILES['linkedin']})
print 'Building features...'
# build features for training data
self.feature_builder.create_feature_vectors(TRAINING_FILES['linkedin'], TRAINING_FEATURE_FILES['linkedin'], 'linkedin')
self.feature_builder.create_feature_vectors(TRAINING_FILES['tweets'], TRAINING_FEATURE_FILES['tweets'], 'tweets')
self.feature_builder.create_feature_vectors(TRAINING_FILES['fb'], TRAINING_FEATURE_FILES['fb'], 'fb')
def retrain_classifier(self):
print 'Training classifier...'
self.classifier = Classifier()
def save_classifier(self):
pickle.dump(self.classifier, open(CLASSIFIER_FILE, 'wb'))
def load_classifier(self):
self.classifier = pickle.load(open(CLASSIFIER_FILE, 'rb'))
def classifier_predict(self):
print 'Parsing Facebook...'
fbparser.parse({'in': TESTING_DIRECTORIES['fb'], 'out': TESTING_FILES['fb']})
print 'Parsing Twitter...'
tweetparser.parse({'in': TESTING_DIRECTORIES['tweets'], 'out': TESTING_FILES['tweets']})
print 'Parsing LinkedIn...'
linkedinparser.parse({'in': TESTING_DIRECTORIES['linkedin'], 'out': TESTING_FILES['linkedin']})
print 'Building features...'
self.feature_builder.create_feature_vectors(TESTING_FILES['linkedin'], TESTING_FEATURE_FILES['linkedin'], 'linkedin')
self.feature_builder.create_feature_vectors(TESTING_FILES['tweets'], TESTING_FEATURE_FILES['tweets'], 'tweets')
self.feature_builder.create_feature_vectors(TESTING_FILES['fb'], TESTING_FEATURE_FILES['fb'], 'fb')
linkedin_testing_features = np.loadtxt(TESTING_FEATURE_FILES['linkedin'], delimiter=',')
tweets_testing_features = np.loadtxt(TESTING_FEATURE_FILES['tweets'], delimiter=',')
fb_testing_features = np.loadtxt(TESTING_FEATURE_FILES['fb'], delimiter=',')
print 'Predicting labels...'
print 'LinkedIn classifier:'
print self.classifier.predict_testing_data('linkedin', linkedin_testing_features, TESTING_LABELS_FILE, 'results_l.txt')
print 'Twitter classifier:'
print self.classifier.predict_testing_data('tweets', tweets_testing_features, TESTING_LABELS_FILE, 'results_t.txt')
tweets_result_labels = np.loadtxt('results_t.txt', delimiter=',')
linkedin_result_labels = np.loadtxt('results_l.txt', delimiter=',')
print 'Late fusion classifier:'
print self.classifier.predict_late_fusion_testing_data([tweets_result_labels, linkedin_result_labels], TESTING_LABELS_FILE, 'result.txt')
class SentimentAnalyzer:
def __init__(self):
self.parser_options = tweetparser.options
self.classifier = Classifier()
if os.path.exists(CLASSIFIER_FILE):
self.classifier.load_classifier(CLASSIFIER_FILE)
else:
self.retrain_classifier()
def rebuild_features(self):
print 'Parsing tweets...'
tweetparser.parse_all_files(self.parser_options)
print 'Building features...'
# build features for training data
training_labels = read_labels(TRAINING)
training_tweets = pickle.load(open(TRAINING_TWEETS, 'rb'))
unigram_features = buildfeatures.build_unigram_feature_dict(training_tweets, training_labels)
training_data = buildfeatures.get_feature_vectors(training_tweets, unigram_features)
# save training data
np.savetxt(TRAINING_DATA_FILE, training_data, delimiter=',')
# build features for testing data
testing_tweets = pickle.load(open(TESTING_TWEETS, 'rb'))
testing_data = buildfeatures.get_feature_vectors(testing_tweets, unigram_features)
np.savetxt(TESTING_DATA_FILE, testing_data, delimiter=',')
# build features for development data
development_tweets = pickle.load(open(DEVELOPMENT_TWEETS, 'rb'))
development_data = buildfeatures.get_feature_vectors(development_tweets, unigram_features)
np.savetxt(DEVELOPMENT_DATA_FILE, development_data, delimiter=',')
# save unigram features processed
pickle.dump(unigram_features, open(UNIGRAM_FEATURES_FILE, 'wb'), -1)
def retrain_classifier(self):
if not os.path.exists(TRAINING_DATA_FILE):
self.rebuild_features()
training_data = np.loadtxt(TRAINING_DATA_FILE, delimiter=',')
training_labels = read_labels(TRAINING)
print 'Training classifier...'
self.classifier = Classifier()
self.classifier.train(training_data, training_labels)
self.classifier.save_classifier(CLASSIFIER_FILE)
def classify_test_tweets(self):
testing_tweets = pickle.load(open(TESTING_TWEETS, 'rb'))
testing_data = np.loadtxt(TESTING_DATA_FILE, delimiter=',')
testing_labels = read_labels(TESTING)
testing_topics = read_topics(TESTING)
print 'Predicting labels...'
print 'Testing Results: ' + str(self.classifier.predict_testing_data(testing_tweets, testing_data, testing_topics, testing_labels, RESULTS_FILE))
def classify_development_tweets(self):
development_tweets = pickle.load(open(DEVELOPMENT_TWEETS, 'rb'))
development_data = np.loadtxt(DEVELOPMENT_DATA_FILE, delimiter=',')
development_labels = read_labels(DEVELOPMENT)
development_topics = read_topics(DEVELOPMENT)
print 'Predicting labels...'
print 'Development Results: ' + str(self.classifier.predict_testing_data(development_tweets, development_data, development_topics, development_labels, RESULTS_FILE))
def classify_custom_tweets(self, custom_filename):
if not os.path.exists(custom_filename):
print 'The file ' + custom_filename + ' does not exist.'
return
try:
print 'Parsing tweets...'
custom_tweets = []
def collect(tweet):
custom_tweets.append(tweet)
tweetparser._parse_tweets(custom_filename, collect)
labels = read_labels(custom_filename)
topics = read_topics(custom_filename)
print 'Building features...'
unigram_features = pickle.load(open(UNIGRAM_FEATURES_FILE, 'rb'))
data = buildfeatures.get_feature_vectors(custom_tweets, unigram_features)
print 'Predicting labels...'
labels = read_labels(custom_filename)
topics = read_topics(custom_filename)
print 'Results: ' + str(self.classifier.predict_testing_data(custom_tweets, data, topics, labels, RESULTS_FILE))
print 'See labels at: ' + RESULTS_FILE
except:
print 'Something went wrong. File may be in wrong format.'
def cross_validation(self):
training_data = np.loadtxt(TRAINING_DATA_FILE, delimiter=',')
training_labels = read_labels(TRAINING)
raw_classifier = self.classifier.get_classifier()
kf_total = cross_validation.KFold(len(training_labels), n_folds=10, shuffle=True, random_state=4)
print 'Average F1-Score: ' + str(np.average(cross_validation.cross_val_score(raw_classifier, training_data, training_labels, cv=kf_total, n_jobs=1, scoring='f1_weighted')))
def adjust_parser(self):
length = len(self.parser_options)
option = 0
while not option == length + 1:
print 'Which parser switch do you want to flip?'
switches = {}
for i, (opt, val) in enumerate(self.parser_options.items()):
switches[i + 1] = opt
print str(i + 1) + '. ' + opt + ':' + (' ' * (24 - len(opt))) + str(val)
print str(length + 1) + '. Back to main menu'
option = input('Answer: ')
if option > 0 and option < length + 1:
opt = switches[option]
self.parser_options[opt] = not self.parser_options[opt]
