摘要
This paper focuses on how to improve aspect-level opinion mining for online customer reviews. We first propose a novel generative topic model, the Joint Aspect/Sentiment (JAS) model, to jointly extract aspects and aspect-dependent sentiment lexicons from online customer reviews. An aspect-dependent sentiment lexicon refers to the aspect-specific opinion words along with their aspect-aware sentiment polarities with respect to a specific aspect. We then apply the extracted aspectdependent sentiment lexicons to a series of aspect-level opinion mining tasks, including implicit aspect identification, aspect-based extractive opinion summarization, and aspect-level sentiment classification. Experimental results demonstrate the effectiveness of the JAS model in learning aspectdependent sentiment lexicons and the practical values of the extracted lexicons when applied to these practical tasks.
This paper focuses on how to im- prove aspect-level opinion mining for online customer reviews. We first propose a novel generative topic model, the Joint Aspect/Sen- timent （JAS） model, to jointly extract aspects and aspect-dependent sentiment lexicons from online customer reviews. An aspect-dependent sentiment lexicon refers to the aspect-specific opinion words along with their aspect-aware sentiment polarities with respect to a specific aspect. We then apply the extracted aspect- dependent sentiment lexicons to a series of as- pect-level opinion mining tasks, including imp- licit aspect identification, aspect-based extrac- tive opinion summarization, and aspect-level sentiment classification. Experimental results demonstrate the effectiveness of the JAS model in learning aspectdependent sentiment lexicons and the practical values of the extracted lexicons when applied to these practical tasks.
基金
supported by National Natural Science Foundation of China under Grants No.61232010, No.60903139, No.60933005, No.61202215, No.61100083
National 242 Project under Grant No.2011F65
China Information Technology Security Evaluation Center Program under Grant No.Z1277
