Sentiment Analysis of Figurative Language using a Word Sense Disambiguation Approach

In this paper we propose a methodology for sentiment analysis of figurative language which applies Word Sense Disambiguation and, through an n-gram graph based method, assigns polarity to word senses. Polarity assigned to senses, combined with contextual valence shifters, is exploited for further assigning polarity to sentences, using Hidden Markov Models. Evaluation results using the corpus of the Affective Text task of SemEval’07, are presented together with a comparison with other state-of-the-art methods, showing that the proposed method provides promising results, and positive evidence supporting our conjecture: figurative language conveys