LyS: Porting a Twitter Sentiment Analysis Approach from Spanish to English

Abstract

[Abstract]: This paper proposes an approach to solve message- and phrase-level polarity classification in Twitter, derived from an existing system designed for Spanish. As a first step, an ad-hoc preprocessing is performed. We then identify lexical, psychological and semantic features in order to capture different dimensions of the human language which are helpful to detect sentiment. These features are used to feed a supervised classifier after applying an information gain filter, to discriminate irrelevant features. The system is evaluated on the SemEval 2014 task 9: Sentiment Analysis in Twitter. Our approach worked competitively both in message- and phraselevel tasks. The results confirm the robustness of the approach, which performed well on different domains involving short informal texts.