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Modified framework for sarcasm detection and classification in sentiment analysis

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 pr...

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Main Authors: Mohd Suhairi Md Suhaimin, Mohd Hanafi Ahmad Hijazi, Rayner Alfred, Frans Coenen
Format: Article
Language:English
English
Published: Institute of Advanced Engineering and Science (IAES) 2018
Subjects:
BF Psychology
HM Sociology (General)
Online Access:https://eprints.ums.edu.my/id/eprint/30046/1/Modified%20framework%20for%20sarcasm%20detection%20and%20classification%20in%20sentiment%20analysis-Abstract.pdf
https://eprints.ums.edu.my/id/eprint/30046/2/Modified%20framework%20for%20sarcasm%20detection%20and%20classification%20in%20sentiment%20analysis.pdf
https://eprints.ums.edu.my/id/eprint/30046/
http://ijeecs.iaescore.com/index.php/IJEECS/article/view/17000
https://doi.org/10.11591/ijeecs.v13.i3.pp1175-1183
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spelling my.ums.eprints.300462021-07-23T01:10:09Z https://eprints.ums.edu.my/id/eprint/30046/ Modified framework for sarcasm detection and classification in sentiment analysis Mohd Suhairi Md Suhaimin Mohd Hanafi Ahmad Hijazi Rayner Alfred Frans Coenen BF Psychology HM Sociology (General) 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. Institute of Advanced Engineering and Science (IAES) 2018-12-17 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/30046/1/Modified%20framework%20for%20sarcasm%20detection%20and%20classification%20in%20sentiment%20analysis-Abstract.pdf text en https://eprints.ums.edu.my/id/eprint/30046/2/Modified%20framework%20for%20sarcasm%20detection%20and%20classification%20in%20sentiment%20analysis.pdf 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) http://ijeecs.iaescore.com/index.php/IJEECS/article/view/17000 https://doi.org/10.11591/ijeecs.v13.i3.pp1175-1183
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic BF Psychology
HM Sociology (General)
spellingShingle BF Psychology
HM Sociology (General)
Mohd Suhairi Md Suhaimin
Mohd Hanafi Ahmad Hijazi
Rayner Alfred
Frans Coenen
Modified framework for sarcasm detection and classification in sentiment analysis
description 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.
format Article
author Mohd Suhairi Md Suhaimin
Mohd Hanafi Ahmad Hijazi
Rayner Alfred
Frans Coenen
author_facet Mohd Suhairi Md Suhaimin
Mohd Hanafi Ahmad Hijazi
Rayner Alfred
Frans Coenen
author_sort Mohd Suhairi Md Suhaimin
title Modified framework for sarcasm detection and classification in sentiment analysis
title_short Modified framework for sarcasm detection and classification in sentiment analysis
title_full Modified framework for sarcasm detection and classification in sentiment analysis
title_fullStr Modified framework for sarcasm detection and classification in sentiment analysis
title_full_unstemmed Modified framework for sarcasm detection and classification in sentiment analysis
title_sort modified framework for sarcasm detection and classification in sentiment analysis
publisher Institute of Advanced Engineering and Science (IAES)
publishDate 2018
url https://eprints.ums.edu.my/id/eprint/30046/1/Modified%20framework%20for%20sarcasm%20detection%20and%20classification%20in%20sentiment%20analysis-Abstract.pdf
https://eprints.ums.edu.my/id/eprint/30046/2/Modified%20framework%20for%20sarcasm%20detection%20and%20classification%20in%20sentiment%20analysis.pdf
https://eprints.ums.edu.my/id/eprint/30046/
http://ijeecs.iaescore.com/index.php/IJEECS/article/view/17000
https://doi.org/10.11591/ijeecs.v13.i3.pp1175-1183
_version_ 1760230711801937920
score 13.211869

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