Sarcasm Detection And Classification To Support Sentiment Analysis: A Study In Malay Social Media

Mohd Hanafi Ahmad Hijazi (2016) Sarcasm Detection And Classification To Support Sentiment Analysis: A Study In Malay Social Media. (Submitted)

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Abstract

This research work conducted on sarcasm detection and classification to support sentiment analysis. The proposed work consists of two phases: (i) sarcasm detection and (ii) sentiment analysis with sarcasm detection and classification. In the first phase, the development of a mechanism for detecting sarcasm on bilingual data was explored. To achieve this, a feature extraction process was proposed to identify sarcasm features. Five feature categories that can be extracted using natural language processing were considered. The best-performing features were then used as input for the second phase. In the second phase, a framework for sentiment analysis that considers sarcasm detection and classification was proposed. Results obtained demonstrate that the proposed features and framework are able to improve the performance of sentiment analysis.

Item Type:	Research Report
Keyword:	Sentiment analysis ,Sarcasm , Clarification , Malay Social Media
Subjects:	H Social Sciences > HA Statistics > HA1-4737 Statistics > HA29-32 Theory and method of social science statistics Q Science > QA Mathematics > QA1-939 Mathematics > QA299.6-433 Analysis
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	NORAINI LABUK -
Date Deposited:	14 Oct 2021 09:43
Last Modified:	14 Oct 2021 10:09
URI:	https://eprints.ums.edu.my/id/eprint/30793

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