In current social tagging system, users can freely add tags for the uploaded resources, which caused a problem that many tags could not describe the resource properly and even have some spelling errors. This problem may bring unnecessary troubles for other users who want to search this kind of resource. In this paper, a tag recommendation system based on collaborative filtering and text similarity is presented to solve the problem mentioned above. This system can automatically recommend some relevant tags for the new uploaded resources and thus the users can freely select tags from the system. Experimental results show that the recommended tags can effectively represent the contents of the webpages marked. Compared with the existing tag recommended methods, this method not only improves the accuracy of tags recommended, but also facilitates the webpage sharing and retrieval.

Product reviews are not only useful for trade companies to improve the quality of products, but also helpful for customers to purchase products reasonably, thus product reviews mining is valuable in application and research. In this paper, we devote the research on book reviews. We first propose a polarity dictionary construction method based on the improved CHI, and realizes dynamic addition of the dictionary; Second, the polarity calculation formula of the transitional complex sentences is improved to be applicable to book reviews. Considering that some book reviews have titles and these titles generally express the reviewers’ opinion tendency, so we further propose an opinion polarity analysis method based on the titles and the improved polarity calculation formula of the heavy transitional sentences. The experimental results show that the approach proposed in this paper is effective.