Paper: UT-DB: An Experimental Study on Sentiment Analysis in Twitter

ACL ID S13-2063
Title UT-DB: An Experimental Study on Sentiment Analysis in Twitter
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

This paper describes our system for participat- ing SemEval2013 Task2-B (Kozareva et al., 2013): Sentiment Analysis in Twitter. Given a message, our system classifies whether the message is positive, negative or neutral senti- ment. It uses a co-occurrence rate model. The training data are constrained to the data pro- vided by the task organizers (No other tweet data are used). We consider 9 types of fea- tures and use a subset of them in our submitted system. To see the contribution of each type of features, we do experimental study on features by leaving one type of features out each time. Results suggest that unigrams are the most im- portant features, bigrams and POS tags seem not helpful, and stopwords should be retained to achieve the best results. The overall results of our system a...