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

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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.

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Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Hong Kong, China

    Jianfeng Si & Qing Li

  2. State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China

    Tieyun Qian

  3. AIMS Lab, Department of Computer Science, Shanghai Jiaotong University, Shanghai, China

    Xiaotie Deng

Authors
  1. Jianfeng Si
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  2. Qing Li
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  3. Tieyun Qian
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  4. Xiaotie Deng
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Correspondence to Jianfeng Si.

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This paper is an extended version of our previous conference paper [32]

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Si, J., Li, Q., Qian, T. et al. Users’ interest grouping from online reviews based on topic frequency and order. World Wide Web 17, 1321–1342 (2014). https://doi.org/10.1007/s11280-013-0239-z

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