TY  - JOUR
PB  - Maxwell Science Publications
SN  - 20407459
EP  - 2410
AV  - none
TI  - A review of unsupervised approaches of opinion target extraction from unstructured reviews
SP  - 2400
N1  - cited By 3
Y1  - 2014///
VL  - 7
JF  - Research Journal of Applied Sciences, Engineering and Technology
A1  - Khan, K.
A1  - Baharudin, B.
A1  - Khan, A.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899065149&doi=10.19026%2frjaset.7.543&partnerID=40&md5=aa1b1290b2fda68bdb13b89f189c243d
ID  - scholars5414
N2  - 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.
IS  - 12
ER  -