NILC_USP: An Improved Hybrid System for Sentiment Analysis in Twitter Messages
Pedro Balage Filho, Lucas Avanço, Thiago Alexandre Salgueiro Pardo, Maria das Graças Volpe Nunes · 2014
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. 1