Abstract

This paper describes our submission to SemEval2014 Task 9: Sentiment Analysis in Twitter. Our model is primarily a lexicon based one, augmented by some preprocessing, 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 features are used to better capture complex phenomena like sarcasm. The resulting supervised system, using a Naive Bayes model, achieved high performance in classifying entire tweets, ranking 7th on the main set and 2nd when applied to sarcastic tweets.

Date

2014

Authors

Nikolaos Malandrakis, Michael Falcone, Colin Vaz, Jesse James Bisogni, Alexandros Potamianos, Shrikanth Narayanan

Conference

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

Pages

512-516