Abstract
Sentiment word identification (SWI) is a basic task of sentiment analysis. Traditional techniques become unqualified because they need seed sentiment words which may lead to low robustness. This paper presents an optimization-based framework by incorporating sentiment contextual information instead of seed words. Specifically, we exploit two sentiment phenomena: (1) sentiment matching: polarities of the document and its most component sentiment words are the same, and (2) sentiment consistency: polarities of two frequently co-occurring words are the same. Empirical results demonstrate that our models significantly outperform the existing approaches.
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