Results 31 to 40 of about 4,714,839 (322)
Sarcasm Detection and Quantification in Arabic Tweets [PDF]
2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), 2021 The role of predicting sarcasm in the text is known as automatic sarcasm detection. Given the prevalence and challenges of sarcasm in sentiment-bearing text, this is a critical phase in most sentiment analysis tasks. With the increasing popularity and usage of different social media platforms among users around the world, people are using sarcasm more ...
Bashar Talafha +4 more
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A Comprehensive Survey on Arabic Sarcasm Detection: Approaches, Challenges and Future Trends
IEEE Access, 2023 On social media platforms, it is essential to express one’s thoughts, opinions, and reviews. One of the most widely used linguistic forms to criticize or express a person’s ideas with ridicule is sarcasm, where the written text has both ...
Alaa Rahma +2 more
doaj +1 more source
BanglaSarc: A Dataset for Sarcasm Detection
2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 2022 Being one of the most widely spoken language in the world, the use of Bangla has been increasing in the world of social media as well. Sarcasm is a positive statement or remark with an underlying negative motivation that is extensively employed in today's social media platforms.
Tasnim Sakib Apon +5 more
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Detecting sarcasm in multi-domain datasets using convolutional neural networks and long short term memory network model [PDF]
PeerJ Computer Science, 2021 Sarcasm emerges as a common phenomenon across social networking sites because people express their negative thoughts, hatred and opinions using positive vocabulary which makes it a challenging task to detect sarcasm.
Ramish Jamil +5 more
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Sarcasm Detection: A Comparative Study
CoRR, 2021 Sarcasm detection is the task of identifying irony containing utterances in sentiment-bearing text. However, the figurative and creative nature of sarcasm poses a great challenge for affective computing systems performing sentiment analysis. This article compiles and reviews the salient work in the literature of automatic sarcasm detection.
Hamed Yaghoobian +2 more
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Transformers on Sarcasm Detection with Context [PDF]
Proceedings of the Second Workshop on Figurative Language Processing, 2020 Sarcasm Detection with Context, a shared task of Second Workshop on Figurative Language Processing (co-located with ACL 2020), is study of effect of context on Sarcasm detection in conversations of Social media. We present different techniques and models, mostly based on transformer for Sarcasm Detection with Context.
Amardeep Kumar, Vivek Anand
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Heliyon, 2023 Sarcasm detection research in Bengali is still limited due to a lack of relevant resources. In this context, getting high-quality annotated data is costly and time-consuming. Therefore, in this paper, we present a transformer-based generative adversarial
Sanzana Karim Lora +4 more
doaj +1 more source
Unfolding Sarcasm in Twitter Using C-RNN Approach
, 2021 Sarcasm detection in text is an inspiring field to explore due to its contradictory behavior. Textual data can be analyzed in order to discover clues those lead to sarcasm.
Akash Mehta, Dutta, Shawni
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Sarcasm Detection is Way Too Easy! An Empirical Comparison of Human and Machine Sarcasm Detection [PDF]
, 2023 Recently, author-annotated sarcasm datasets, which focus on intended, rather than perceived sarcasm, have been introduced. Although datasets collected using first-party annotation have important benefits, there is no comparison of human and machine ...
Oprea, Silviu Vlad +3 more
core +2 more sources
Sarcasm Detection on Czech and English Twitter.
, 2014 This paper presents a machine learning approach to sarcasm detection on Twitter in two languages – English and Czech. Although there has been some research in sarcasm detection in languages other than English (e.g., Dutch, Italian, and Brazilian Portuguese), our work is the first attempt at sarcasm detection in the Czech language.
Ptáek, Tomáš +2 more
core +4 more sources