A survey of automatic sarcasm detection: Fundamental theories, formulation, datasets, detection methods, and opportunities

Chen, Wangqun; Lin, Fuqiang; Li, Guowei; Liu, Bo

doi:10.1016/j.neucom.2024.127428

Neurocomputing

2024

DOI: 10.1016/j.neucom.2024.127428

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A survey of automatic sarcasm detection: Fundamental theories, formulation, datasets, detection methods, and opportunities

Wangqun Chen,

Fuqiang Lin,

Guowei Li

et al.

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2024

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Cited by 2 publications

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IdSarcasm: Benchmarking and Evaluating Language Models for Indonesian Sarcasm Detection

Suhartono,

Wongso,

Tri Handoyo

2024

IEEE Access

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IdSarcasm: Benchmarking and Evaluating Language Models for Indonesian Sarcasm Detection

Suhartono,

Wongso,

Tri Handoyo

2024

IEEE Access

View full text Add to dashboard Cite

S ³ Agent: Unlocking the Power of VLLM for Zero-Shot Multi-modal Sarcasm Detection

Wang,

Zhang,

Fei

et al. 2024

ACM Trans. Multimedia Comput. Commun. Appl.

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Multi-modal sarcasm detection involves determining whether a given multi-modal input conveys sarcastic intent by analyzing the underlying sentiment. Recently, vision large language models have shown remarkable success on various of multi-modal tasks. Inspired by this, we systematically investigate the impact of vision large language models in zero-shot multi-modal sarcasm detection task. Furthermore, to capture different perspectives of sarcastic expressions, we propose a multi-view agent framework, S 3 Agent, designed to enhance zero-shot multi-modal sarcasm detection by leveraging three critical perspectives: superficial expression , semantic information , and sentiment expression . Our experiments on the MMSD2.0 dataset, which involves six models and four prompting strategies, demonstrate that our approach achieves state-of-the-art performance. Our method achieves an average improvement of 13.2% in accuracy. Moreover, we evaluate our method on the text-only sarcasm detection task, where it also surpasses baseline approaches.

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A survey of automatic sarcasm detection: Fundamental theories, formulation, datasets, detection methods, and opportunities

Cited by 2 publications

References 63 publications

IdSarcasm: Benchmarking and Evaluating Language Models for Indonesian Sarcasm Detection

IdSarcasm: Benchmarking and Evaluating Language Models for Indonesian Sarcasm Detection

S ³ Agent: Unlocking the Power of VLLM for Zero-Shot Multi-modal Sarcasm Detection

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A survey of automatic sarcasm detection: Fundamental theories, formulation, datasets, detection methods, and opportunities

Cited by 2 publications

References 63 publications

IdSarcasm: Benchmarking and Evaluating Language Models for Indonesian Sarcasm Detection

IdSarcasm: Benchmarking and Evaluating Language Models for Indonesian Sarcasm Detection

S 3 Agent: Unlocking the Power of VLLM for Zero-Shot Multi-modal Sarcasm Detection

Contact Info

Product

Resources

About

S ³ Agent: Unlocking the Power of VLLM for Zero-Shot Multi-modal Sarcasm Detection