Users’ interest grouping from online reviews based on topic frequency and order

Abstract

Large volume of online review data can reveal consumers' major interests on domain product, which attracts great research interests from the academic community. Most of the existing works focus on the problems of review summarization, aspect identification or opinion mining from an item's point of view such as the quality or popularity of products. Considering the fact that users who generate those review texts draw different attentions to product aspects with respect to their own interests, in this article, we aim to learn K users' interest groups indicated by their review writings. Such K interest groups' identification can facilitate better understanding of major and potential consumers' concerns which are crucial for applications like product improvement on customer-oriented design or diverse marketing strategies. Instead of using a traditional text clustering approach, we treat the groupId/clusterId as a hidden variable and use a permutation-based structural topic model called KMM. Through this model, we infer K interest groups' distribution by discovering not only the frequency of product aspects (Topic Frequency), but also the occurrence priority of respective aspects (Topic Order). They jointly present an informative summarization on the raw review corpus. Our experiment on several real-world review datasets demonstrates a competitive solution.