Mining Opinion Targets from Text Documents: A Review
Khairullah Khan1,2,
Baharum B. Baharudin1, and
Aurangzeb Khan2
1. Computer and Information Sciences Department, Universiti Teknologi PETRONAS, Malaysia
2. Institute of Engineering and Computing Sciences, University of Science & Technology Bannu Pakistan
2. Institute of Engineering and Computing Sciences, University of Science & Technology Bannu Pakistan
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 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.
Index Terms—opinion mining, sentiment analysis, opinion targets, machine learning
Cite: Khairullah Khan, Baharum B. Baharudin, and Aurangzeb Khan, "Mining Opinion Targets from Text Documents: A Review," Journal of Emerging Technologies in Web Intelligence, Vol. 5, No. 4, pp. 343-353, November 2013. doi:10.4304/jetwi.5.4.343-353
Index Terms—opinion mining, sentiment analysis, opinion targets, machine learning
Cite: Khairullah Khan, Baharum B. Baharudin, and Aurangzeb Khan, "Mining Opinion Targets from Text Documents: A Review," Journal of Emerging Technologies in Web Intelligence, Vol. 5, No. 4, pp. 343-353, November 2013. doi:10.4304/jetwi.5.4.343-353
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