Research Article | OPEN ACCESS
A Review of Unsupervised Approaches of Opinion Target Extraction from Unstructured Reviews
Khairullah Khan, Baharum Baharudin and Aurangzeb Khan
CIS Department, University Technology PETRONAS, Malaysia
Research Journal of Applied Sciences, Engineering and Technology 2014 12:2400-2410
Received: July 27, 2012 | Accepted: September 03, 2012 | Published: March 29, 2014
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.
Keywords:
Features extraction, machine learning, opinion mining, opinion targets, sentiment analysis,
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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The authors have no competing interests.
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