Unveiling sarcastic intent: web-based detection of sarcasm in news / Mohd Nazzim Lahaji, Tajul Rosli Razak and Mohammad Hafiz Ismail

• Main Authors: Lahaji, Mohd Nazzim (Author), Razak, Tajul Rosli (Author), Ismail, Mohammad Hafiz (Author)
• Format: Book
• Published: UiTM Cawangan Perlis, 2023.
• Subjects: Article; PeerReviewed
• Online Access: Link Metadata
• Notes: https://ir.uitm.edu.my/id/eprint/86879/1/86879.pdf
• Keywords: Algorithms

Detecting sarcasm in news headlines poses a significant challenge due to the intricate nature of language and the subtle nuances of sarcastic expressions. This study uses machine learning techniques to introduce a novel web-based sarcasm detection system tailored explicitly for news headlines. This study's key novelty and contribution lie in addressing the domain-specific problem of sarcasm detection in news headlines, which has received limited attention in previous research. The proposed algorithm effectively distinguishes between sarcastic and non-sarcastic headlines by analysing the semantic features of words and the underlying attitude conveyed by the headline's structure. Data pre-processing played a critical role in preparing the dataset for analysis and modelling, ensuring the accuracy and reliability of the system. A comparative study was conducted to validate the system's performance, benchmarking it against existing approaches. The results demonstrate the superiority of the developed model in sarcasm detection for news headlines. The system's unique output classifies sarcastic words into low, moderate, or high probabilities of being sarcastic, providing valuable insights into the intensity of sarcasm. Notably, the system is userfriendly and versatile, capable of processing diverse inputs effectively. The significance of this study lies in its novel contribution to the field of sarcasm detection in news headlines. By addressing the specific challenges of this domain, the developed system enhances the overall understanding and interpretation of news content. It is a valuable tool for individuals and news organisations, enabling swift and reliable identification of sarcasm in headlines, thereby enriching news comprehension and analysis.