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• W3166202376 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." @default.
• W3166202376 created "2021-06-22" @default.
• W3166202376 creator A5032680041 @default.
• W3166202376 creator A5090718537 @default.
• W3166202376 date "2021-01-01" @default.
• W3166202376 modified "2023-09-28" @default.
• W3166202376 title "A Survey on Aspect Extraction Approaches for Sentiment Analysis" @default.
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• W3166202376 doi "https://doi.org/10.4018/978-1-7998-7371-6.ch003" @default.
• W3166202376 hasPublicationYear "2021" @default.
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