This paper describes our system for participating 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 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.
|Title of host publication||Proceedings of the Seventh International Workshop on Semantic Evaluation, SemEval 2013|
|Place of Publication||USA|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||6|
|ISBN (Print)||not assigned|
|Publication status||Published - Jun 2013|
|Publisher||Association for Computational Linguistics|
- Sentiment Analysis
Zhu, Z., Hiemstra, D., Apers, P. M. G., & Wombacher, A. (2013). UT-DB: an experimental study on sentiment analysis in twitter. In Proceedings of the Seventh International Workshop on Semantic Evaluation, SemEval 2013 (pp. 384-389). USA: Association for Computational Linguistics (ACL).