Improving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training
Bing Xiang, Liang Zhou · 2014
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. 1