Paper: SAIL: Sentiment Analysis using Semantic Similarity and Contrast Features

ACL ID S14-2089
Title SAIL: Sentiment Analysis using Semantic Similarity and Contrast Features
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

This paper describes our submission to Se- mEval2014 Task 9: Sentiment Analysis in Twitter. Our model is primarily a lexi- con based one, augmented by some pre- processing, including detection of Multi- Word Expressions, negation propagation and hashtag expansion and by the use of pairwise semantic similarity at the tweet level. Feature extraction is repeated for sub-strings and contrasting sub-string fea- tures are used to better capture complex phenomena like sarcasm. The resulting supervised system, using a Naive Bayes model, achieved high performance in clas- sifying entire tweets, ranking 7th on the main set and 2nd when applied to sarcastic tweets.