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

ACL ID D09-1062
Title Adapting a Polarity Lexicon using Integer Linear Programming for Domain-Specific Sentiment Classification
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

Polarity lexicons have been a valuable re- source for sentiment analysis and opinion mining. There are a number of such lexi- cal resources available, but it is often sub- optimal to use them as is, because general purpose lexical resources do not reflect domain-specific lexical usage. In this pa- per, we propose a novel method based on integer linear programming that can adapt an existing lexicon into a new one to re- flect the characteristics of the data more directly. In particular, our method collec- tivelyconsidersthe relationsamongwords and opinionexpressionsto derive themost likely polarity of each lexical item (posi- tive, neutral, negative, or negator) for the given domain. Experimental results show that our lexicon adaptation technique im- provestheperformance offine-grainedpo- l...