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.
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.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||Opinion Mining, Customer reviews, Conditional random fields, Product reviews, Feature Function|
|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:||18 Mar 2016 12:44|
Repository Staff Only: item control page