Unveiling Sarcastic Intent: Web-Based Detection of Sarcasm In News Headlines

Mohd Nazzim Lahaji; Tajul Rosli Razak; Mohammad Hafiz bin Ismail

doi:10.24191/jcrinn.v8i2.365

Unveiling Sarcastic Intent: Web-Based Detection of Sarcasm In News Headlines

Authors

Mohd Nazzim Lahaji College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, MALAYSIA
Tajul Rosli Razak College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, MALAYSIA
Mohammad Hafiz bin Ismail Universiti Teknologi MARA, Perlis Branch

DOI:

https://doi.org/10.24191/jcrinn.v8i2.365

Keywords:

Sarcasm Detection, News Headline, Machine Learning, Web Application

Abstract

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 user-friendly 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.

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References

Ashwitha, A., Shruthi, G., Shruthi, H. R., Upadhyaya, M., Ray, A. P., & Manjunath, T. C. (2021). Sarcasm detection in natural language processing. Materials Today: Proceedings, 37(Part 2), 3324–3331. https://doi.org/10.1016/J.MATPR.2020.09.124

Eddy, S. R. (2004). What is Bayesian statistics? Nature Biotechnology, 22(9), 1177–1178. https://doi.org/10.1038/NBT0904-1177

Gupta, S., Singh, R., & Singla, V. (2020). Emoticon and text sarcasm detection in sentiment analysis. Advances in Intelligent Systems and Computing, 1045, 1–10. https://doi.org/10.1007/978-981-15-0029-9_1/COVER

Lamba, M., & Madhusudhan, M. (2022). Sentiment Analysis. Text Mining for Information Professionals, 191–211. https://doi.org/10.1007/978-3-030-85085-2_7

Lea Michele Reveals How She’d React If Barbra Streisand Saw Her In “Funny Girl.” (n.d.). Retrieved July 7, 2023, from https://www.huffpost.com/entry/lea-michele-streisand-funny-girl_n_63c26450e4b0d6724fce2640

Misra, R., & Arora, P. (2023). Sarcasm detection using news headlines dataset. AI Open, 4, 13–18. https://doi.org/10.1016/J.AIOPEN.2023.01.001

Misra, R., & Grover, J. (2021). Sculpting Data for ML: The first act of Machine Learning.

Sagnika, S., Pattanaik, A., Mishra, B. S. P., & Meher, S. K. (2020). A review on multi-lingual sentiment analysis by machine learning methods. Journal of Engineering Science and Technology Review, 13(2), 154. https://doi.org/10.25103/jestr.132.19

Sentiment Accuracy: Explaining the Baseline and How to Test It - Lexalytics. (n.d.). Retrieved July 7, 2023, from https://www.lexalytics.com/blog/sentiment-accuracy-baseline-testing/

Shrikhande, P., Setty, V., & Sahani, A. (2020). Sarcasm detection in newspaper headlines. 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS), 483–487.

Thakur Sakshi and Singh, S. and S. M. (2020). Detecting Sarcasm in Text. In A. K. and M. P. and G. N. Abraham Ajith and Cherukuri (Ed.), Intelligent Systems Design and Applications (pp. 996–1005). Springer International Publishing.

Vaishanvi, S., Rajkaran, Y. P., Rahul, V., & Nirmal, L. M. (2022). Product Recommendation Using Sentiment Analysis. 8th International Conference on Engineering and Emerging Technologies, ICEET 2022. https://doi.org/10.1109/ICEET56468.2022.10007234

Zain Naqvi, S. (n.d.). SENTIMENT ANALYSIS OF PRODUCT REVIEWS AT DOCUMENT LEVEL.

Zhang, B. J., Quick, R., Helmi, A., & Fitter, N. T. (2020). Socially assistive robots at work: Making break-taking interventions more pleasant, enjoyable, and engaging. IEEE International Conference on Intelligent Robots and Systems, 11292–11299. https://doi.org/10.1109/IROS45743.2020.9341291