def is_reputation_affecting(tweet):
senti_score = TweetSentimentEvaluator.rate_sentiment(tweet)
negative = senti_score[len(senti_score) - 1]
positive = senti_score[0]
if int(negative) >= 2 or int(positive) >= 2:
return True
else:
return False
def is_reputation_affecting(tweet):
senti_score = TweetSentimentEvaluator.rate_sentiment(tweet)
negative = senti_score[len(senti_score) - 1]
positive = senti_score[0]
if int(negative) >= 2 or int(positive) >= 2:
return True
else:
return False
def run(self):
while True:
status = self.tweet_queue.get()
try:
if not self.spam_detector.is_spam(status) and utility.is_reputation_affecting(status):
id_str = status['id_str']
status['reputation_dimension'] = self.dimension_classifier.classify(status)
status['related_entity'] = utility.fetch_entity(status)
status['sentiment_score'] = TweetSentimentEvaluator.rate_sentiment(status)
self.tweets_buffer.append(Tweet(tweet_id=id_str, tweet=status, created_at=timezone.now()))
print 'Tweet %s added to buffer, buffer size %d' % (id_str, len(self.tweets_buffer))
if len(self.tweets_buffer) >= self.buffer_size:
Tweet.objects.bulk_create(self.tweets_buffer)
del self.tweets_buffer[:]
print 'INFO: Bulk created 100 tweets %s' % timezone.now()
except KeyError:
print 'INFO: Error message %s' % status
