Word sense disambiguation based sentiment lexicons for sentiment classification

Abstract

Sentiment analysis has attracted much attention from both researchers and practitioners as word-of-mouth (WOM) has a significant influence on consumer behavior. One core task of sentiment analysis is the discovery of sentimental words. This can be done efficiently when an accurate and large-scale sentiment lexicon is used. SentiWordNet is one such lexicon which defines each synonym set within WordNet with sentiment scores and orientation. As human language is ambiguous, an exact sense for a word in SentiWordNet needs to be justified according to the context in which the word occurs. However, most sentiment-based classification tasks extract sentimental words from SentiWordNet without dealing with word sense disambiguation (WSD), but directly adopt the sentiment score of the first sense or average sense. This paper proposes three WSD techniques based on the context of WOM documents to build WSD-based SentiWordNet lexicons. The experiments demonstrate that an improvement is achieved when the proposed WSD-based SentiWordNet is used.