Effectiveness of web search results for genre and sentiment classification

The motivation of this study is to enhance general topical search with a sentiment-based one where the search results (snippets) returned by the web search engine are clustered by sentiment categories. Firstly we developed an automatic method to identify product review documents using the snippets (summary information that includes the URL, title, and summary text), which is genre classification. Then the identified snippets were automatically classified into positive (recommended) and negative (non-recommended) documents, which is sentiment classification. Thereafter the user may directly decide to access the positive or negative review documents. In this study we used only the snippets rather than their original full-text documents, and applied a common machine learning technique, SVM (support vector machine), and heuristic approaches to investigate how effectively the snippets can be used for genre and sentiment classification. The results show that the web search engine should improve the quality of the snippets especially for opinionated documents (i.e. review documents).