IIIT-H at SemEval 2015: Twitter Sentiment Analysis The good, the bad and the neutral!

This paper describes the system that was sub-mitted to SemEval2015 Task 10: Sentiment Analysis in Twitter. We participated in Sub-task B: Message Polarity Classification. The task is a message level classification of tweets into positive, negative and neutral sentiments. Our model is primarily a supervised one which consists of well designed features fed into an SVM classifier. In previous runs of this task, it was found that lexicons played an important role in determining the sentiment of a tweet. We use existing lexicons to extract lexicon specific features. The lexicon based features are further augmented by tweet specific fea-tures. We also improve our system by using acronym and emoticon dictionaries. The pro-posed system achieves an F1 score of 59.83 and 67.04 on the Test Data and Progress Data respectively. This placed us at the 18th posi-tion for the Test Dataset and the 16th position for the Progress Test Dataset.