Structured feature extraction using association rules

Tian, Nan, Xu, Yue, Li, Yuefeng, & Pasi, Gabriella (2013) Structured feature extraction using association rules. In Lecture Notes in Computer Science, Springer Berlin Heidelberg, Gold Coast, Australia, pp. 270-282.

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As of today, opinion mining has been widely used to iden- tify the strength and weakness of products (e.g., cameras) or services (e.g., services in medical clinics or hospitals) based upon people's feed- back such as user reviews. Feature extraction is a crucial step for opinion mining which has been used to collect useful information from user reviews. Most existing approaches only find individual features of a product without the structural relationships between the features which usually exists. In this paper, we propose an approach to extract features and feature relationship, represented as tree structure called a feature hi- erarchy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature hierarchy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that the proposed feature extraction approach can identify more correct features than the baseline model. Even though the datasets used in the experiment are about cameras, our work can be ap- plied to generate features about a service such as the services in hospitals or clinics.

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ID Code: 67059
Item Type: Conference Paper
Refereed: Yes
Additional Information: Trends and Applications in Knowledge Discovery and Data Mining : PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Gold Coast, QLD, Australia, April 14-17, 2013, Revised Selected Papers
Additional URLs:
Keywords: Feature Extraction, Opinion Mining, Association Rules, Feature Hierarchy, User Reviews
DOI: 10.1007/978-3-642-40319-4_24
ISBN: 9783642403194
ISSN: 0302-9743
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 2013 Springer-Verlag Berlin Heidelberg
Copyright Statement: The final publication is available at
Deposited On: 06 Feb 2014 22:38
Last Modified: 10 Feb 2014 16:56

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