Paper: NILC_USP: An Improved Hybrid System for Sentiment Analysis in Twitter Messages

ACL ID S14-2074
Title NILC_USP: An Improved Hybrid System for Sentiment Analysis in Twitter Messages
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

This paper describes the NILC USP sys- tem that participated in SemEval-2014 Task 9: Sentiment Analysis in Twitter, a re-run of the SemEval 2013 task under the same name. Our system is an improved version of the system that participated in the 2013 task. This system adopts a hybrid classification process that uses three clas- sification approaches: rule-based, lexicon- based and machine learning. We sug- gest a pipeline architecture that extracts the best characteristics from each classi- fier. In this work, we want to verify how this hybrid approach would improve with better classifiers. The improved system achieved an F-score of 65.39% in the Twit- ter message-level subtask for 2013 dataset (+ 9.08% of improvement) and 63.94% for 2014 dataset.