With booming electronic commerce, online reviews are often created by users like who buys a product or goes to a restaurant. However, littery and unordered free-text reviews make it difficult for new users to acquire and analyze useful information. Thus, recommendation system plays an increasingly important role in online surfing. Nowadays, it has been proved that recommendation system based on topics is an available method in the theory and practice. However, there is little study to extract preferences from the perspective of sentiment. The method we proposed is to combine the topics and sentiments for generating a user's preference from the user's previous reviews. According to the degree of similarity with public's preference, recommendation system we proposed would judge whether it should recommend the new products to this user. The empirical results show that the recommendation system we proposed can make accurately and effectively recommend.
['Nieqing Cao', 'Jingjing Cao', 'Panpan Liu', 'Wenfeng Li']
Sentimental Preference Extraction from Online Reviews for Recommendation