Discovering K web user groups with specific aspect interests

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

4 Scopus Citations
View graph of relations

Author(s)

  • Jianfeng Si
  • Qing Li
  • Tieyun Qian
  • Xiaotie Deng

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationMachine Learning and Data Mining in Pattern Recognition
Subtitle of host publication8th International Conference, MLDM 2012, Proceedings
PublisherSpringer Verlag
Pages321-335
Volume7376 LNAI
ISBN (print)9783642315367
Publication statusPublished - 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7376 LNAI
ISSN (Print)0302-9743
ISSN (electronic)1611-3349

Conference

Title8th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2012
PlaceGermany
CityBerlin
Period13 - 20 July 2012

Abstract

Online review analysis becomes a hot research topic recently. 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 and popularity of products. Considering the fact that authors of these review texts may pay different attentions to different domain-based product aspects with respect to their own interests, in this paper, we aim to learn K user groups with specific aspect interests indicated by their review writings. Such K user groups' identification can facilitate better understanding of customers' interests which are crucial for application like product improvement on customer-oriented design or diverse marketing strategies. Instead of using a traditional text clustering approach, we treat the clusterId as a hidden variable and use a permutation-based structural topic model called KMM. Through this model, we infer K groups' distribution by discovering not only the frequency of reviewers' product aspects, but also the occurrence priority of respective aspects. Our experiment on several real-world review datasets demonstrates a competitive solution. © 2012 Springer-Verlag.

Citation Format(s)

Discovering K web user groups with specific aspect interests. / Si, Jianfeng; Li, Qing; Qian, Tieyun et al.
Machine Learning and Data Mining in Pattern Recognition: 8th International Conference, MLDM 2012, Proceedings. Vol. 7376 LNAI Springer Verlag, 2012. p. 321-335 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7376 LNAI).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review