def __init__(self, trainset=[]):

# initializes a SVM classifier

self.classifier = SVM(type=CLASSIFICATION, kernel=LINEAR)

self.bag_of_words = []

self.classifier.probability = True

self.train(self.classifier,trainset)

# Extract features for ML process

def extract_features(self, tweet_message):

if len(self.bag_of_words) == 0:

printf('Bag-of-Words empty!')

return None

tweet_words = [word.lower() for word, tag in tweet_message if word not in stopwords and not word.isdigit()]

tweet_tags = [tag[:2] for word, tag in tweet_message if word not in stopwords and not word.isdigit()]

feature_set = {}

# 1st set of features: bag-of-words

for word in self.bag_of_words:

feature_set['has_'+word] = (word in tweet_words)

# 2nd set of features: the tags present in the message

for tag in ['NN','VG','CD','JJ','CC','RB']:

feature_set['has_'+tag] = (tag in tweet_tags)

# 3rd feature: negation is present?

negators = set(['not', 'none', 'nobody', 'never', 'nothing', 'lack', 't','n\'t','dont', 'no'])

if len(negators.intersection(set(tweet_words))) > 0:

feature_set['has_negator'] = True

return feature_set

# train the classifier

# Tweets argument must be a list of dicitionaries. Each dictionary must

# have the keys ['MESSAGE'] and ['SENTIMENT'] with the message string and

# the classificationclass, respectively.

def train(self,classifier,tweets):

# build the bag-of-words list using the 1k most frequent words in

# the corpus

bag_of_words = {}

for tweet in tweets:

words = [w.lower() for w,t in pre_process(tweet['MESSAGE']) if w not in stopwords and not w.isdigit()]

for word in words:

bag_of_words[word] = bag_of_words.get(word,0) + 1

# get the 1000 most frequent words

self.bag_of_words = [w for w,freq in sorted(bag_of_words.items(),key=itemgetter(1),reverse=True)[:1000]]

# perform the training step

for tweet in tweets:

classifier.train(self.extract_features(pre_process(tweet['MESSAGE'])),type=tweet['SENTIMENT'])

# classify a new message. Return the scores (probabilities) for each

# classification class

def classify(self, tweet_message):

scores = self.classifier.classify(self.extract_features(tweet_message))