Gather customer concerns from online product reviews - A text summarization approach

Product reviews possess critical information regarding customers' concerns and their experience with the product. Such information is considered essential to firms' business intelligence which call be utilized for the purpose of conceptual design, personalization, product recommendation. better customer understanding, and finally attract more loyal customers. Previous Studies of deriving useful information from customer reviews focused mainly oil numerical and categorical data. Textual data have been somewhat ignored although they are deemed valuable. Existing methods of opinion mining ill processing customer reviews concentrates Oil counting Positive and negative comments of review writers, which is not enough to cover ail important topics and concerns across different review articles. Instead, we propose an automatic summarization approach based oil the analysis of review articles' internal topic structure to assemble Customer concerns. Different from the existing summarization approaches centered oil sentence ranking and Clustering, our approach discovers and extracts salient topics from a set of online reviews and further ranks these topics. The final summary is then generated based oil the ranked topics. The experimental study and evaluation show that the proposed approach Outperforms the peer approaches, i.e. opinion mining and clustering-summarization, in terms of users responsiveness and its ability to discover the most important topics. (C) 2007 Elsevier Ltd. All rights reserved.