KELabTeam: A Statistical Approach on Figurative Language Sentiment Analysis in Twitter

In this paper, we propose a new statistical method for sentiment analysis of figurative language within short texts collected from Twitter (called tweets) as a part of SemEval2015 Task 11. Particularly, the proposed model focuses on classifying the tweets into three categories (i.e., sarcastic, ironic, and metaphorical tweet) by extracting two main features (i.e., term features and emotion patterns). Our experiments have been conducted with two datasets, which are Trial set (1000 tweets) and Test set (4000 tweets). Performance is evaluated by cosine similarity to gold annotations. Using this evaluation methodology, the proposed method achieves 0.74 on the Trial set. On the Test set, we achieve 0.90 on sarcastic tweets and 0.89 on ironic tweets.