Rich Lexical Features for Sentiment Analysis on Twitter

In this paper, we describe our system for the Sentiment Analysis of Twitter shared task in SemEval 2014. Our system uses an SVM classifier along with rich set of lexical features to detect the sentiment of a phrase within a tweet (Task-A) and also the sentiment of the whole tweet (Task-B). We start from the lexical features that were used in the 2013 shared tasks, we en-hance the underlying lexicon and also in-troduce new features. We focus our fea-ture engineering effort mainly on Task-A. Moreover, we adapt our initial frame-work and introduce new features for Task-B. Our system reaches weighted score of 87.11 % in Task-A and 64.52 % in Task-B. This places us in the 4th rank in the Task-A and 15th in the Task-B.