Product feature taxonomy learning based on user reviews

Tian, Nan, Xu, Yue, Li, Yuefeng, Abdel-Hafez, Ahmad, & Josang, Audun (2014) Product feature taxonomy learning based on user reviews. In WEBIST 2014 10th International Conference on Web Information Systems and Technologies, 3 - 5 April 2014, Barcelona, Spain.

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In recent years, the Web 2.0 has provided considerable facilities for people to create, share and exchange information and ideas. Upon this, the user generated content, such as reviews, has exploded. Such data provide a rich source to exploit in order to identify the information associated with specific reviewed items. Opinion mining has been widely used to identify the significant features of items (e.g., cameras) based upon user reviews. Feature extraction is the most critical step to identify useful information from texts. Most existing approaches only find individual features about a product without revealing the structural relationships between the features which usually exist. In this paper, we propose an approach to extract features and feature relationships, represented as a tree structure called feature taxonomy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature taxonomy 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 our proposed approach is able to capture the product features and relations effectively.

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1 citations in Scopus
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ID Code: 67058
Item Type: Conference Paper
Refereed: No
Additional URLs:
Keywords: Feature Extraction, Opinion Mining, Association Rules, Feature Taxonomy, User Reviews
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 2014 Please consult the authors
Deposited On: 06 Feb 2014 22:44
Last Modified: 29 Apr 2015 17:48

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