As online shopping is becoming commonplace, more and more product information and product reviews are posted on the Internet. Because customers cannot see and feel the products directly, product reviews are becoming an essential source of qualitative information. As a result, the volume of reviews is increasing drastically and review summarization (or opinion mining) is becoming an important tool for a practical utilization. Presented in this paper is a holistic approach to product review summarization which improves on existing methods. In extracting product features and opinion words, we present a flexible extraction tool that can be customized according to the characteristics of source documents. For sentiment analysis and feature scoring, we utilize the rate scores, which are overall ratings of the product by users. Feature dependent sentiment polarities of opinion words are also utilized. Experiments show that these considerations improve the accuracy of product review summarization.
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