Identifying Product Features from Customer Reviews Using Hybrid Dependency Patterns

Khairullah Khan1, Baharum Baharudin1, and Aurangzeb Khan2
1Computer and Information Sciences Department, Universiti Teknologi PETRONAS, Malaysia
2Institute of Engineering and Computing Sciences, University of Science & Technology Bannu, Pakistan

Abstract: In this paper we have addressed the problem of automatic identification of product features from customer reviews. Costumers, retailors, and manufacturers are popularly using customer reviews on websites for product reputation and sales forecasting. Opinion Mining application have been potentially employed to summarize the huge collectionof customer reviews for decision making. In this paper we have proposed hybrid dependency patterns to extract product features from unstructured reviews. The proposed dependency patterns exploit lexical relations and opinion context to identify features. Based on empirical analysis we found that the proposed hybrid patterns provide comparatively more accurate results. The average precision and recall are significantly improved with hybrid patterns.

Keywords: Opinion mining, features extraction, syntactic relation,context dependency.

 

Received February 3, 2012; accepted January 22, 2012

  

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