Aspect-based opinion extraction from customer reviews

Samha, Amani, Li, Yuefeng, & Zhang, Jinglan (2014) Aspect-based opinion extraction from customer reviews. In Computer Science and Information Technology (CS and IT), Volume 4, Number 4: Proceedings of the Second International Conference of Database and Data Mining (DBDM 2014), AIRCC Publishing Corporation, Dubai, UAE, pp. 149-160.

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Text is the main method of communicating information in the digital age. Messages, blogs, news articles, reviews, and opinionated information abounds on the Internet. People commonly purchase products online and post their opinions about purchased items. This feedback is displayed publicly to assist others with their purchasing decisions, creating the need for a mechanism with which to extract and summarize useful information for enhancing the decision-making process. Our contribution is to improve the accuracy of extraction by combining different techniques from three major areas, named Data Mining, Natural Language Processing techniques and Ontologies. The proposed framework sequentially mines product’s aspects and users’ opinions, groups representative aspects by similarity, and generates an output summary. This paper focuses on the task of extracting product aspects and users’ opinions by extracting all possible aspects and opinions from reviews using natural language, ontology, and frequent “tag” sets. The proposed framework, when compared with an existing baseline model, yielded promising results.

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ID Code: 77870
Item Type: Conference Paper
Refereed: Yes
Keywords: Data Mining, Opinion Mining, Sentiment Analysis, Aspect Extraction, Customer Reviews
ISBN: 978-1-921987-30-4
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 author]
Deposited On: 20 Oct 2014 23:07
Last Modified: 22 Oct 2014 11:48

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