Sentiment word identification using the maximum entropy model

Abstract

This paper addresses the issue of sentiment word identification given an opinionated sentence, which is very important in sentiment analysis tasks. The most common way to tackle this problem is to utilize a readily available sentiment lexicon such as HowNet or SentiWordNet to determine whether a word is a sentiment word. However, in practice, words existing in the lexicon sometimes can not express sentiment tendency in a certain context while other words out of the lexicon do express. To address this challenge, this paper presents an approach based on maximum-entropy classification model to identify sentiment words given an opinionated sentence. Experimental results show that our approach outperforms baseline lexicon-based methods.