Mining e-commerce feedback comments for dimension rating profiles

Lishan Cui, Xiuzhen Zhang, Yan Wang, Lifang Wu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

1 Citation (Scopus)


Opinion mining on regular documents like movie reviews and product reviews has been intensively studied. In this paper we focus on opinion mining on short e-commerce feedback comments.We aim to compute a comprehensive rating profile for sellers comprising of dimension ratings and weights. We propose an algorithm to mine feedback comments for dimension ratings, combining opinion mining and dependency relation analysis, a recent development in natural language processing. We formulate the problem of computing dimension weights from ratings as a factor analytic problem and propose an effective solution based on matrix factorisation. Extensive experiments on eBay and Amazon data demonstrate that our proposed algorithms can achieve accuracies of 93.1% and 89.64% respectively for identifying dimensions and ratings in feedback comments, and the weights computed can accurately reflect the amount of feedback for dimensions.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications
Subtitle of host publication9th International Conference, ADMA 2013, Hangzhou, China, December 14-16, 2013, Proceedings, Part 1
EditorsHiroshi Motoda, Zhaohui Wu, Longbing Cao, Osmar Zaiane, Min Yao, Wei Wang
Place of PublicationHeidelberg
PublisherSpringer, Springer Nature
Number of pages12
ISBN (Electronic)9783642539145
ISBN (Print)9783642539138
Publication statusPublished - 2013
Event9th International Conference on Advanced Data Mining and Applications, ADMA 2013 - Hangzhou, China
Duration: 14 Dec 201316 Dec 2013

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other9th International Conference on Advanced Data Mining and Applications, ADMA 2013


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