Sarcasm Identification and Detection in Conversion Context using BERT

A. Kalaivani, D. Thenmozhi
{"title":"Sarcasm Identification and Detection in Conversion Context using BERT","authors":"A. Kalaivani, D. Thenmozhi","doi":"10.18653/v1/2020.figlang-1.10","DOIUrl":null,"url":null,"abstract":"Sarcasm analysis in user conversion text is automatic detection of any irony, insult, hurting, painful, caustic, humour, vulgarity that degrades an individual. It is helpful in the field of sentimental analysis and cyberbullying. As an immense growth of social media, sarcasm analysis helps to avoid insult, hurts and humour to affect someone. In this paper, we present traditional machine learning approaches, deep learning approach (LSTM -RNN) and BERT (Bidirectional Encoder Representations from Transformers) for identifying sarcasm. We have used the approaches to build the model, to identify and categorize how much conversion context or response is needed for sarcasm detection and evaluated on the two social media forums that is twitter conversation dataset and reddit conversion dataset. We compare the performance based on the approaches and obtained the best F1 scores as 0.722, 0.679 for the twitter forums and reddit forums respectively.","PeriodicalId":160021,"journal":{"name":"Fig-Lang@ACL","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fig-Lang@ACL","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/2020.figlang-1.10","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14

Abstract

Sarcasm analysis in user conversion text is automatic detection of any irony, insult, hurting, painful, caustic, humour, vulgarity that degrades an individual. It is helpful in the field of sentimental analysis and cyberbullying. As an immense growth of social media, sarcasm analysis helps to avoid insult, hurts and humour to affect someone. In this paper, we present traditional machine learning approaches, deep learning approach (LSTM -RNN) and BERT (Bidirectional Encoder Representations from Transformers) for identifying sarcasm. We have used the approaches to build the model, to identify and categorize how much conversion context or response is needed for sarcasm detection and evaluated on the two social media forums that is twitter conversation dataset and reddit conversion dataset. We compare the performance based on the approaches and obtained the best F1 scores as 0.722, 0.679 for the twitter forums and reddit forums respectively.
基于BERT的转换语境反讽识别与检测
用户转换文本中的讽刺分析是自动检测任何讽刺,侮辱,伤害,痛苦,刻薄,幽默,粗俗,降低个人。它在情感分析和网络欺凌领域很有帮助。随着社交媒体的迅猛发展,讽刺分析有助于避免侮辱、伤害和幽默影响他人。在本文中,我们提出了传统的机器学习方法,深度学习方法(LSTM -RNN)和BERT(来自变压器的双向编码器表示)来识别讽刺。我们已经使用这些方法来构建模型,以识别和分类挖苦检测需要多少转换上下文或响应,并在两个社交媒体论坛上进行评估,即twitter会话数据集和reddit转换数据集。我们比较了两种方法的性能,twitter论坛和reddit论坛的F1得分最高,分别为0.722和0.679。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信