An approach to rank reviews by fusing and mining opinions based on review pertinence

Fusing and mining opinions from reviews posted in webs or social networks is becoming a popular research topic in recent years in order to analyze public opinions on a specific topic or product. Existing research has been focused on extraction, classification and summarization of opinions from reviews in news websites, forums and blogs. An important issue that has not been well studied is the degree of relevance between a review and its corresponding article. Prior work simply divides reviews into two classes: spam and non-spam, neglecting that the non-spam reviews could have different degrees of relevance to the article. In this paper, we propose a notion of “Review Pertinence” to study the degree of this relevance. Unlike usual methods, we measure the pertinence of review by considering not only the similarity between a review and its corresponding article, but also the correlation among reviews. Experiment results based on real data sets collected from a number of popular portal sites show the obvious effectiveness of our method in ranking reviews based on their pertinence, compared with three baseline methods. Thus, our method can be applied to efficiently retrieve reviews for opinion fusion and mining and filter review spam in practice.