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

J. Si, Qing Li, T. Qian, X. Deng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)


© 2013, Springer Science+Business Media New York. 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.
Original languageEnglish
Pages (from-to)1321-1342
Number of pages22
JournalWorld Wide Web
Issue number6
Publication statusPublished - 1 Jan 2014
Externally publishedYes


  • Interest grouping
  • Review analysis
  • Structural topic modeling

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


Dive into the research topics of 'Users' interest grouping from online reviews based on topic frequency and order'. Together they form a unique fingerprint.

Cite this