eprintid: 5414
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/54/14
datestamp: 2023-11-09 16:17:09
lastmod: 2023-11-09 16:17:09
status_changed: 2023-11-09 16:01:35
type: article
metadata_visibility: show
creators_name: Khan, K.
creators_name: Baharudin, B.
creators_name: Khan, A.
title: A review of unsupervised approaches of opinion target extraction from unstructured reviews
ispublished: pub
note: cited By 3
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 study presents a review of the state of the art unsupervised approaches for opinion target identification due to its potential applications in opinion mining from web documents. This study compares the existing approaches that might be helpful in the future research work of opinion mining and features extraction. © Maxwell Scientific Organization, 2014.
date: 2014
publisher: Maxwell Science Publications
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899065149&doi=10.19026%2frjaset.7.543&partnerID=40&md5=aa1b1290b2fda68bdb13b89f189c243d
id_number: 10.19026/rjaset.7.543
full_text_status: none
publication: Research Journal of Applied Sciences, Engineering and Technology
volume: 7
number: 12
pagerange: 2400-2410
refereed: TRUE
issn: 20407459
citation:   Khan, K. and Baharudin, B. and Khan, A.  (2014) A review of unsupervised approaches of opinion target extraction from unstructured reviews.  Research Journal of Applied Sciences, Engineering and Technology, 7 (12).  pp. 2400-2410.  ISSN 20407459