Paper: Improving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training

ACL ID P14-2071
Title Improving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

In this paper, we present multiple ap- proaches to improve sentiment analysis on Twitter data. We first establish a state-of-the-art baseline with a rich fea- ture set. Then we build a topic-based sen- timent mixture model with topic-specific data in a semi-supervised training frame- work. The topic information is generated through topic modeling based on an ef- ficient implementation of Latent Dirich- let Allocation (LDA). The proposed sen- timent model outperforms the top system in the task of Sentiment Analysis in Twit- ter in SemEval-2013 in terms of averaged F scores.