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Mining opinion targets from text documents: A review
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.
Journal of Emerging Technologies in Web Intelligence
HTML Summary of #3370 Mining opinion targets from text documents: A review