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Abstract
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Article Information:
Identifying Product Features from Customer Reviews using Lexical Concordance
Khairullah Khan and Baharum B. Baharudin
Corresponding Author: khairullah
Submitted: 2011 December, 06
Accepted: 2011 December, 26
Published: 2012 April, 01 |
Abstract:
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Automatic extraction of features from unstructured text is one of the challenging problems of
Opinion Mining. The trend of getting products and services reputation from online resources such as web blogs
and customer feedback is increasing day by day. Therefore efficient system is required to automatically extract
products features and the opinion of consumers about all aspects of the products. In this study our focus is on
extraction of product features from customer reviews. We have proposed a concordance based technique for
automatic extraction of features of product from customer reviews. In our proposed technique we extract
patterns of lexical terms using concordance for candidate features extraction and identify features by grouping.
The proposed grouping algorithm is used to remove irrelevant features. We conducted experiments on different
products reviews and compared our results with existing methods. From empirical results we proved the validity
of the proposed method.
Key words: Concordance, feature extraction, feature grouping, opinion mining, , ,
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Cite this Reference:
Khairullah Khan and Baharum B. Baharudin, . Identifying Product Features from Customer Reviews using Lexical Concordance. Research Journal of Applied Sciences, Engineering and Technology, (07): 833-839.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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