Incorporating phrase-level sentiment analysis on textual reviews for personalized recommendation

Previous research on Recommender Systems (RS), especially the continuously popular approach of Collaborative Filtering (CF), has been mostly focusing on the information resource of explicit user numerical ratings or implicit (still numerical) feedbacks. However, the ever-growing availability of textual user reviews has become an important information resource, where a wealth of explicit product attributes/features and user attitudes/sentiments are expressed therein. This infor-mation rich resource of textual reviews have clearly exhib-ited brand-new approaches to solving many of the important problems that have been perplexing the research community for years, such as the paradox of cold-start, the explana-tion of recommendation, and the automatic generation of user or item profiles. However, it is only recently that the fundamental importance of textual reviews has gained wide recognition, perhaps mainly because of the difficulty in for-matting, structuring and analyzing the free-texts. In this research, we stress the importance of incorporating textual reviews for recommendation through phrase-level sentiment analysis, and further investigate the role that the texts play in various important recommendation tasks.