An efficient approach for sentence-based opinion retrieval

Recently, there is a growing interest in sharing personal opinions on the Web, such as product reviews, economic analysis, political polls, etc. Therefore, opinion retrieval, which targets to retrieve documents expressing opinions or comments about the query, has become more and more popular. A typical method for opinion retrieval is document-based and each document is assigned a relevant score and an opinionated score, respectively. Then the documents are ranking based on a combination of the two scores. In this method, however, the document is split into bag-of-word, and the association between the opinion and its corresponding target is broken. In an extreme case, a relevant document full of irrelevant opinions will also be retrieved. In this paper, we propose a sentence-based approach since opinions are always expressed in one sentence where the association between an opinion and its corresponding target is maintained. We assign an individual score to each sentence rather than assign an overall score to the document directly. Moreover, we consider the effectiveness of different positions of sentences in documents to further capture the structural information. Compared with document-based approaches, experimental results on our own dataset show that our approach has achieved significant improvement.