MSRM: A Novel Model to Retrieve Meaningful Opinion Sentences for New Products

Online reviews are more and more important for potential consumers to make purchase decisions. The referred information of the new products or unpopular products that have no reviews or very few reviews is limited and this situation makes it difficult for consumers to obtain enough information to understand these products. Indeed, this is a new issue recently that needs to be addressed. In this paper, we study the problem of automatically retrieving meaningful opinion sentences for a new product or unpopular product from reviews of other similar products. The retrieved meaningful opinion sentences should possess three properties: helpfulness, relevance and coverage. We propose a meaningful sentences retrieval model (MSRM), which is centered on these three properties to extract meaningful opinion sentences for a new product or unpopular product. We employ product specifications to estimate similarity between products, and model helpfulness, relevance and coverage properties respectively, finally incorporate these three properties into MSRM to mine meaningful sentences from reviews of similar products. Through a series of experiments on real data sets, experiment results show MSRM achieves much better performance.