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Advancements in Sarcasm Detection: A Comprehensive Survey of Latest Techniques and Challenges Integrating Machine Learning, Deep Learning Methods

Manjusha P D

Abstract


Sarcasm, a figurative language expressing senti- ments contrary to its literal meaning, poses a unique challenge in natural language processing. This survey navigates the intri- cate landscape of sarcasm detection, synthesizing methodologies across machine learning, deep learning, corpus-based techniques, and the integration of psycholinguistic features. Our exploration aims to offer a holistic understanding of the current state of the field, emphasizing the effectiveness of machine learning in capturing linguistic nuances, the transformative impact of deep learning in unraveling complex structures, insights from corpus-based analyses, and the emerging trend of incorporating psycholinguistic features for enhanced cognitive understanding. Focusing on the latest techniques, strengths, and challenges within each approach, this paper serves as a roadmap for collab- orative advancements and delineates future research directions in the dynamic realm of sarcasm detection.


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