Modified framework for sarcasm detection and classification in sentiment analysis

Mohd Suhairi Md Suhaimin and Mohd Hanafi Ahmad Hijazi and Rayner Alfred and Frans Coenen (2018) Modified framework for sarcasm detection and classification in sentiment analysis. Indonesian Journal of Electrical Engineering and Computer Science, 13 (3). pp. 1175-1183. ISSN 2502-4752 (P-ISSN) , 2502-4760 (E-ISSN)

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Abstract

Sentiment analysis is directed at identifying people's opinions, beliefs, views and emotions in the context of the entities and attributes that appear in text. The presence of sarcasm, however, can significantly hamper sentiment analysis. In this paper a sentiment classification framework is presented that incorporates sarcasm detection. The framework was evaluated using a nonlinear Support Vector Machine and Malay social media data. The results obtained demonstrated that the proposed sarcasm detection process could successfully detect the presence of sarcasm in that better sentiment classification performance was recorded. A best average F-measure score of 0.905 was recorded using the framework; a significantly better result than when sentiment classification was performed without sarcasm detection.

Item Type:	Article
Keyword:	Classification , Framework , Malay social media , Sarcasm detection , Sentiment analysis
Subjects:	B Philosophy. Psychology. Religion > BF Psychology H Social Sciences > HM Sociology (General)
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	DG MASNIAH AHMAD -
Date Deposited:	23 Jul 2021 09:10
Last Modified:	23 Jul 2021 09:10
URI:	https://eprints.ums.edu.my/id/eprint/30046

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