Splusplus: A Feature-Rich Two-stage Classifier for Sentiment Analysis of Tweets

This paper describes our sentiment classifica-tion system submitted to SemEval-2015 Task 10. In the message-level polarity classifica-tion subtask, we obtain the highest macro-averaged F1-scores on three out of six test-ing sets. Specifically, we build a two-stage classifier to predict the sentiment labels for tweets, which enables us to design different features for subjective/objective classification and positive/negative classification. In addi-tion to n-grams, lexicons, word clusters, and twitter-specific features, we develop several deep learning methods to automatically ex-tract features for the message-level sentiment classification task. Moreover, we propose a polarity boosting trick which improves the performance of our system.