Generating product feature hierarchy from product reviews

Tian, Nan, Xu, Yue, Li, Yuefeng, Abdel-Hafez, Ahmad, & Josang, Audun (2015) Generating product feature hierarchy from product reviews. In Monfort, V. & Krempels, K.H. (Eds.) Web Information Systems and Technologies. Springer International Publishing, pp. 264-278.

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User generated information such as product reviews have been booming due to the advent of web 2.0. In particular, rich information associated with reviewed products has been buried in such big data. In order to facilitate identifying useful information from product (e.g., cameras) reviews, opinion mining has been proposed and widely used in recent years. In detail, as the most critical step of opinion mining, feature extraction aims to extract significant product features from review texts. However, most existing approaches only find individual features rather than identifying the hierarchical relationships between the product features. In this paper, we propose an approach which finds both features and feature relationships, structured as a feature hierarchy which is referred to as feature taxonomy in the remainder of the paper. Specifically, by making use of frequent patterns and association rules, we construct the feature taxonomy to profile the product at multiple levels instead of single level, which provides more detailed information about the product. The experiment which has been conducted based upon some real world review datasets shows that our proposed method is capable of identifying product features and relations effectively.

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ID Code: 92872
Item Type: Book Chapter
Additional Information: Proceedings of 10th International Conference, WEBIST 2014, Barcelona, Spain, April 3-5, 2014.
Keywords: Feature extraction, Opinion mining, Association rules, Feature taxonomy, User reviews
DOI: 10.1007/978-3-319-27030-2_17
ISBN: 9783319270296
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 Springer
Deposited On: 12 Feb 2016 04:57
Last Modified: 15 Feb 2016 00:52

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