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This is a Literature Review study.

Advancing Sarcasm Detection: The Case for Naturalistic Conversational Datasets

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This review analyzes sarcasm detection datasets, emphasizing naturalistic data for sentiment analysis and AI applications.

Natural language processing and sarcasm detection are essential for effective sentiment analysis and conversational AI.
Analysis of 46 research papers reveals three types of sarcasm detection datasets: text-based, visual-textual, and audio-visual.
Multimodal datasets are crucial for improving sarcasm recognition in various contexts, including social media and dialogues.
Addressing limitations in audio sarcasm data can enhance model accuracy, vital for real-world applications like healthcare and virtual assistants.

Awasthi et al. (2025) studied this question.

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