Investigating the challenges of sarcasm prediction

This project delves deep into the intricate nature of sarcasm detection in Natural Language Processing (NLP). Sarcasm, being a complex linguistic construct, often relies on contextual cues and incongruities, making its automatic detection particularly challenging in textual data. This research aims...

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Bibliographic Details
Main Author: Chua, Rachel Jing Wen
Other Authors: Wang Wenya
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/181034
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Institution: Nanyang Technological University
Language: English
Description
Summary:This project delves deep into the intricate nature of sarcasm detection in Natural Language Processing (NLP). Sarcasm, being a complex linguistic construct, often relies on contextual cues and incongruities, making its automatic detection particularly challenging in textual data. This research aims to identify and dissect the multifaceted challenges posed by sarcasm in computational models. The study will analyse the performance of DL models in sarcasm prediction, examine the role of context, the effect of additional knowledge, and possibly explore potential strategies to overcome identified challenges.