Discovering K web user groups with specific aspect interests
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | Machine Learning and Data Mining in Pattern Recognition |
Subtitle of host publication | 8th International Conference, MLDM 2012, Proceedings |
Publisher | Springer Verlag |
Pages | 321-335 |
Volume | 7376 LNAI |
ISBN (print) | 9783642315367 |
Publication status | Published - 2012 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 7376 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (electronic) | 1611-3349 |
Conference
Title | 8th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2012 |
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Place | Germany |
City | Berlin |
Period | 13 - 20 July 2012 |
Link(s)
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).
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 Works › RGC 32 - Refereed conference paper (with host publication) › peer-review