Adapting a Polarity Lexicon using Integer Linear Programming for Domain-Specific Sentiment Classification

Polarity lexicons have been a valuable resource for sentiment analysis and opinion mining. There are a number of such lexical resources available, but it is often suboptimal to use them as is, because general purpose lexical resources do not reflect domain-specific lexical usage. In this paper, we propose a novel method based on integer linear programming that can adapt an existing lexicon into a new one to reflect the characteristics of the data more directly. In particular, our method collectively considers the relations among words and opinion expressions to derive the most likely polarity of each lexical item (positive, neutral, negative, or negator) for the given domain. Experimental results show that our lexicon adaptation technique improves the performance of fine-grained polarity classification.