Aspect-based opinion mining from product reviews using conditional random fields
Data(s) |
08/08/2015
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Resumo |
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. |
Formato |
application/pdf |
Identificador | |
Publicador |
Australian Computer Society |
Relação |
http://eprints.qut.edu.au/86572/1/Aspect%20based%20Opinion%20Mining%20using%20CRF%20by%20Amani%20Samha%20.pdf http://ausdm.org/publication.html 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. |
Direitos |
Copyright 2015 [please consult the authors] |
Fonte |
Science & Engineering Faculty |
Palavras-Chave | #Opinion Mining #Customer reviews #Conditional random fields #Product reviews #Feature Function |
Tipo |
Conference Paper |