科研成果详情

This paper focuses on structured Web review extraction and opinion summarization. An opinion extraction framework and a feature-opinion based opinion summarization algorithm (FOOSA) are proposed. Opinion related Web pages are first clustered according to their link and title similarities. Then reviews are extracted using tag patterns which are generated through one instance learning method. The extracted reviews are filtered basically according to some rules to remove meaningless symbols. At last, all reviews are segmented into feature-opinion sentences for continual processing. In opinion summarization, FOOSA uses feature-opinion pairs as keys to construct feature-opinion (FO) buckets. Then we use inter bucket reducing method based on information entropy to remove duplicated sentences in different buckets. FOOSA also carries out intra bucket clustering to select representative sentences from every bucket. FOOSA builds positive and negative text opinion summaries through our ranking method. A structured review extraction and opinion summarization system is implemented based on our approach. Our system provides not only text opinion summarization, but also Web based graphical overall sentiment summarization. Experiments show our review extraction and opinion summarization approaches are effective and promising. © 2009 Binary Information Press March, 2009.