UT-DB: An Experimental Study on Sentiment Analysis in Twitter
Zhemin Zhu, Djoerd Hiemstra, Peter Apers, and Andreas Wombacher
This paper describes our system for participating SemEval 2013 Task 2-B: Sentiment Analysis in Twitter. Given a message, our system classifies whether the message is positive, negative or neutral sentiment. It uses a co-occurrence rate model. The training data are constrained to the data provided by the task organizers (No other tweet data are used). We consider 9 types of features 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 important features, bigrams and POS tags seem not helpful, and stopwords should be retained to achieve the best results. The overall results of our system are promising regarding the constrained features and data we use.