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Using LLMs to Infer Non-Binary COVID-19 Sentiments of Chinese Microbloggers. [PDF]
Entropy (Basel)Hu JC, Modi MS, Szymanski BK.
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KnowleNet: Knowledge fusion network for multimodal sarcasm detection
Information Fusion, 2023Sarcasm is a form of communication often used to express contempt or ridicule, where the speaker conveys a message opposite to their true meaning, typically intending to mock or belittle a specific target.
Rui Mao, Erik Cambria, Tan Yue
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Speech Communication, 2008
The present study was conducted to identify possible acoustic cues of sarcasm. Native English speakers produced a variety of simple utterances to convey four different attitudes: sarcasm, humour, sincerity, and neutrality. Following validation by a separate naive group of native English speakers, the recorded speech was subjected to acoustic analyses ...
Marc D. Pell, Henry S Cheang
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The present study was conducted to identify possible acoustic cues of sarcasm. Native English speakers produced a variety of simple utterances to convey four different attitudes: sarcasm, humour, sincerity, and neutrality. Following validation by a separate naive group of native English speakers, the recorded speech was subjected to acoustic analyses ...
Marc D. Pell, Henry S Cheang
exaly +3 more sources
Sarcasm Suite: A Browser-Based Engine for Sarcasm Detection and Generation
Proceedings of the AAAI Conference on Artificial Intelligence, 2017 Sarcasm Suite is a browser-based engine that deploys five of our past papers in sarcasm detection and generation. The sarcasm detection modules use four kinds of incongruity: sentiment incongruity, semantic incongruity, historical context incongruity and conversational context incongruity. The sarcasm generation module is a chatbot that
Aditya Joshi 0001 +3 more
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Perceptual and Motor Skills, 2001 This study examined facial expression in the presentation of sarcasm. 60 responses (sarcastic responses = 30, nonsarcastic responses = 30) from 40 different speakers were coded by two trained coders. Expressions in three facial areas—eyebrow, eyes, and mouth—were evaluated. Only movement in the mouth area significantly differentiated ratings of sarcasm
Patricia Rockwell
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The paradox of sarcasm: Theory of mind and sarcasm use in adults
Personality and Individual Differences, 2020Abstract While there is evidence suggesting that sarcasm comprehension is positively associated with the theory of mind (ToM), it remains unclear whether ToM ability predicts sarcasm use in any way. A random sample of 171 undergraduate students participated in an open-ended ToM measure, namely the social faux pas stories, and a purpose-designed ...
Zhenlin Wang, Ning Zhu
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Sarcasm-GPT: advancing sarcasm detection with large language models
The Computer JournalAbstract Sarcasm detection is a nuanced challenge in natural language processing, requiring deep understanding of textual and contextual cues. We present Sarcasm-GPT, a large language model-based model that integrates four key components: prompt template generation, retrieval-augmented generation, chain-of-thought generation, and a ...
Qiuyu Li +4 more
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