eprintid: 5386
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/53/86
datestamp: 2023-11-09 16:17:07
lastmod: 2023-11-09 16:17:07
status_changed: 2023-11-09 16:01:30
type: article
metadata_visibility: show
creators_name: Khan, K.
creators_name: Baharudin, B.
creators_name: Khan, A.
title: Identifying product features from customer reviews using hybrid patterns
ispublished: pub
note: cited By 22
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.
date: 2014
publisher: Zarka Private Univ
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84900015678&partnerID=40&md5=010658ce2b6361f696dcce1a34884b4f
full_text_status: none
publication: International Arab Journal of Information Technology
volume: 11
number: 3
pagerange: 281-286
refereed: TRUE
issn: 16833198
citation:   Khan, K. and Baharudin, B. and Khan, A.  (2014) Identifying product features from customer reviews using hybrid patterns.  International Arab Journal of Information Technology, 11 (3).  pp. 281-286.  ISSN 16833198