eprintid: 3370
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/33/70
datestamp: 2023-11-09 15:51:37
lastmod: 2023-11-09 15:51:37
status_changed: 2023-11-09 15:46:40
type: article
metadata_visibility: show
creators_name: Khan, K.
creators_name: Baharudin, B.B.
creators_name: Khan, A.
title: Mining opinion targets from text documents: A review
ispublished: pub
note: cited By 4
abstract: Opinion targets identification is an important task of the opinion mining problem. Several approaches have been employed for this task, which can be broadly divided into two major categories: supervised and unsupervised. The supervised approaches require training data, which need manual work and are mostly domain dependent. The unsupervised technique is most popularly used due to its two main advantages: domain independent and no need for training data. This paper presents a review of the state of the art unsupervised approaches for opinion target identification due to its potential applications in opinion mining from user discourse. This study compares the existing approaches that might be helpful in the future research work of opinion mining and features extraction. © 2013 ACADEMY PUBLISHER.
date: 2013
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84891594033&doi=10.4304%2fjetwi.5.4.343-353&partnerID=40&md5=d15b2ac09ee8a0384ad6360a82870f5c
id_number: 10.4304/jetwi.5.4.343-353
full_text_status: none
publication: Journal of Emerging Technologies in Web Intelligence
volume: 5
number: 4
pagerange: 343-353
refereed: TRUE
issn: 17980461
citation:   Khan, K. and Baharudin, B.B. and Khan, A.  (2013) Mining opinion targets from text documents: A review.  Journal of Emerging Technologies in Web Intelligence, 5 (4).  pp. 343-353.  ISSN 17980461