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A Survey on Aspect Extraction Approaches for Sentiment Analysis
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Author(s): Vrps Sastry Yadavilli (National Institute of Technology, Tadepalligudem, India)and Karthick Seshadri (National Institute of Technology, Tadepalligudem, India)
Copyright: 2021
Pages: 24
Source title:
Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance
Source Author(s)/Editor(s): Dipti P. Rana (Sardar Vallabhbhai National Institute of Technology, Surat, India)and Rupa G. Mehta (Sardar Vallabhbhai National Institute of Technology, Surat, India)
DOI: 10.4018/978-1-7998-7371-6.ch003
PurchaseView on the publisher's website for pricing and purchasing information.
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Abstract
Aspect-level sentiment analysis gives a detailed view of user opinions expressed towards each feature of a product. Aspect extraction is a challenging task in aspect-level sentiment analysis. Hence, several researchers worked on the problem of aspect extraction during the past decade. The authors begin this chapter with a brief introduction to aspect-level sentimental analysis, which covers the definition of key terms used in this chapter, and the authors also illustrate various subtasks of aspect-level sentiment analysis. The introductory section is followed by an explanation of the various feature learning methods like supervised, unsupervised, semi-supervised, etc. with a discussion regarding their merits and demerits. The authors compare the aspect extraction methods performance with respect to metrics and a detailed discussion on the merits and demerits of the approaches. They conclude the chapter with pointers to the unexplored problems in aspect-level sentiment analysis that may be beneficial to the researchers who wish to pursue work in this challenging and mature domain.
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