Aspect-based opinion mining from product reviews using conditional random fields

Samha, Amani K., Li, Yuefeng, & Zhang, Jinglan (2015) Aspect-based opinion mining from product reviews using conditional random fields. In Data Mining and Analytics: Proceedings of the 13th Australasian Data Mining Conference [Conferences in Research and Practice in Information Technology, Volume 168], Australian Computer Society, University of Technology, Sydney, Australia, pp. 119-128.

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Product reviews are the foremost source of information for customers and manufacturers to help them make appropriate purchasing and production decisions. Natural language data is typically very sparse; the most common words are those that do not carry a lot of semantic content, and occurrences of any particular content-bearing word are rare, while co-occurrences of these words are rarer. Mining product aspects, along with corresponding opinions, is essential for Aspect-Based Opinion Mining (ABOM) as a result of the e-commerce revolution. Therefore, the need for automatic mining of reviews has reached a peak. In this work, we deal with ABOM as sequence labelling problem and propose a supervised extraction method to identify product aspects and corresponding opinions. We use Conditional Random Fields (CRFs) to solve the extraction problem and propose a feature function to enhance accuracy. The proposed method is evaluated using two different datasets. We also evaluate the effectiveness of feature function and the optimisation through multiple experiments.

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ID Code: 86572
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Opinion Mining, Customer reviews, Conditional random fields, Product reviews, Feature Function
ISBN: 978-1-921770-18-0
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 [please consult the authors]
Deposited On: 17 Aug 2015 01:19
Last Modified: 28 Jun 2017 19:23

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