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Abstract
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Article Information:
A Review of Unsupervised Approaches of Opinion Target Extraction from Unstructured Reviews
Khairullah Khan, Baharum Baharudin and Aurangzeb Khan
Corresponding Author: khairullah
Submitted: July 27, 2012
Accepted: September 03, 2012
Published: March 29, 2014 |
Abstract:
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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.
Key words: Features extraction, machine learning, opinion mining, opinion targets, sentiment analysis, ,
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Cite this Reference:
Khairullah Khan, Baharum Baharudin and Aurangzeb Khan, . A Review of Unsupervised Approaches of Opinion Target Extraction from Unstructured Reviews. Research Journal of Applied Sciences, Engineering and Technology, (12): 2400-2410.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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