A DEEP LEARNING APPROACH TO SARCASM DETECTION FROM COMPOSITE TEXTUAL DATA
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | INFOCOMP: Jornal de Ciência da Computação |
Texto Completo: | https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/2266 |
Resumo: | Sarcasm is a means of conveying a bad attitude through social media platforms by utilizing positive or exaggerated positive terms. The last decade witnessed sarcasm detection to become a highly phenomenal topic of research; however the task of automated detection of sarcastic comments in a text remains an elusive problem. Sarcasm detection has eventually become a considerably significant task in the domain of sentiment classification. Without properly detecting the sarcasm from textual comments, sentiment classification remains incomplete and may lead to wrongful conclusion and decision. In this paper, we present a recurrent neural network (RNN)-based bidirectional long-short term memory (Bi-LSTM) network for sarcasm detection.The proposed technique has been applied to a combined dataset which is produced form news headline sarcasm dataset and news headline sarcasm version 2. Results of our technique renders enhanced performance over the existing technique found in literature. |
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INFOCOMP: Jornal de Ciência da Computação |
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A DEEP LEARNING APPROACH TO SARCASM DETECTION FROM COMPOSITE TEXTUAL DATASarcasm is a means of conveying a bad attitude through social media platforms by utilizing positive or exaggerated positive terms. The last decade witnessed sarcasm detection to become a highly phenomenal topic of research; however the task of automated detection of sarcastic comments in a text remains an elusive problem. Sarcasm detection has eventually become a considerably significant task in the domain of sentiment classification. Without properly detecting the sarcasm from textual comments, sentiment classification remains incomplete and may lead to wrongful conclusion and decision. In this paper, we present a recurrent neural network (RNN)-based bidirectional long-short term memory (Bi-LSTM) network for sarcasm detection.The proposed technique has been applied to a combined dataset which is produced form news headline sarcasm dataset and news headline sarcasm version 2. Results of our technique renders enhanced performance over the existing technique found in literature.Editora da UFLA2022-12-19info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/2266INFOCOMP Journal of Computer Science; Vol. 21 No. 2 (2022): December 20221982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/2266/586Copyright (c) 2022 Amit Khan Amit Khan, DIPANKAR MAJUMDAR, BIKROMADITTYA MONDAL, SOUMEN MUKHERJEEinfo:eu-repo/semantics/openAccessKhan, AmitDIPANKAR MAJUMDAR BIKROMADITTYA MONDALSOUMEN MUKHERJEE2022-12-19T14:48:47Zoai:infocomp.dcc.ufla.br:article/2266Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:47.989756INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
A DEEP LEARNING APPROACH TO SARCASM DETECTION FROM COMPOSITE TEXTUAL DATA |
title |
A DEEP LEARNING APPROACH TO SARCASM DETECTION FROM COMPOSITE TEXTUAL DATA |
spellingShingle |
A DEEP LEARNING APPROACH TO SARCASM DETECTION FROM COMPOSITE TEXTUAL DATA Khan, Amit |
title_short |
A DEEP LEARNING APPROACH TO SARCASM DETECTION FROM COMPOSITE TEXTUAL DATA |
title_full |
A DEEP LEARNING APPROACH TO SARCASM DETECTION FROM COMPOSITE TEXTUAL DATA |
title_fullStr |
A DEEP LEARNING APPROACH TO SARCASM DETECTION FROM COMPOSITE TEXTUAL DATA |
title_full_unstemmed |
A DEEP LEARNING APPROACH TO SARCASM DETECTION FROM COMPOSITE TEXTUAL DATA |
title_sort |
A DEEP LEARNING APPROACH TO SARCASM DETECTION FROM COMPOSITE TEXTUAL DATA |
author |
Khan, Amit |
author_facet |
Khan, Amit DIPANKAR MAJUMDAR BIKROMADITTYA MONDAL SOUMEN MUKHERJEE |
author_role |
author |
author2 |
DIPANKAR MAJUMDAR BIKROMADITTYA MONDAL SOUMEN MUKHERJEE |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Khan, Amit DIPANKAR MAJUMDAR BIKROMADITTYA MONDAL SOUMEN MUKHERJEE |
description |
Sarcasm is a means of conveying a bad attitude through social media platforms by utilizing positive or exaggerated positive terms. The last decade witnessed sarcasm detection to become a highly phenomenal topic of research; however the task of automated detection of sarcastic comments in a text remains an elusive problem. Sarcasm detection has eventually become a considerably significant task in the domain of sentiment classification. Without properly detecting the sarcasm from textual comments, sentiment classification remains incomplete and may lead to wrongful conclusion and decision. In this paper, we present a recurrent neural network (RNN)-based bidirectional long-short term memory (Bi-LSTM) network for sarcasm detection.The proposed technique has been applied to a combined dataset which is produced form news headline sarcasm dataset and news headline sarcasm version 2. Results of our technique renders enhanced performance over the existing technique found in literature. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-12-19 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/2266 |
url |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/2266 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/2266/586 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2022 Amit Khan Amit Khan, DIPANKAR MAJUMDAR, BIKROMADITTYA MONDAL, SOUMEN MUKHERJEE info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2022 Amit Khan Amit Khan, DIPANKAR MAJUMDAR, BIKROMADITTYA MONDAL, SOUMEN MUKHERJEE |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da UFLA |
publisher.none.fl_str_mv |
Editora da UFLA |
dc.source.none.fl_str_mv |
INFOCOMP Journal of Computer Science; Vol. 21 No. 2 (2022): December 2022 1982-3363 1807-4545 reponame:INFOCOMP: Jornal de Ciência da Computação instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
INFOCOMP: Jornal de Ciência da Computação |
collection |
INFOCOMP: Jornal de Ciência da Computação |
repository.name.fl_str_mv |
INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
infocomp@dcc.ufla.br||apfreire@dcc.ufla.br |
_version_ |
1799874742691299328 |