Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques

  • Neha Singh
    Department of ITCA, Madan Mohan Malaviya University of Technology, Gorakhpur, India nehaps2703[at]
  • Umesh Chandra Jaiswal
    Department of ITCA, Madan Mohan Malaviya University of Technology, Gorakhpur, India


One of the biggest problems with sentiment analysis systems is sarcasm. The use of implicit, indirect language to express opinions is what gives it its complexity. Sarcasm can be represented in a number of ways, such as in headings, conversations, or book titles. Even for a human, recognizing sarcasm can be difficult because it conveys feelings that are diametrically contrary to the literal meaning expressed in the text. There are several different models for sarcasm detection. To identify humorous news headlines, this article assessed vectorization algorithms and several machine learning models. The recommended hybrid technique using the bag-of-words and TF-IDF feature vectorization models is compared experimentally to other machine learning approaches. In comparison to existing strategies, experiments demonstrate that the proposed hybrid technique with the bag-of-word vectorization model offers greater accuracy and F1-score results.
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Aboobaker, J., & Ilavarasan, E. (2020). A Survey on Sarcasm detection and challenges. Proc. of 6th Intl. Conf. on Advanced Computing & Communication Systems, 1234–1240.

Abulaish, M., & Kamal, A. (2018). Self-deprecating sarcasm detection: an amalgamation of rule-based and machine learning approach. 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 574–579.

Alexandru, D., & Gîfu, D. (2020). Tracing humor in edited news headlines. Ludic, Co-Design and Tools Supporting Smart Learning Ecosystems and Smart Education: Proceedings of the 5th International Conference on Smart Learning Ecosystems and Regional Development, 187–196.

Aneja, S., Aneja, N., & Kumaraguru, P. (2022). Predictive linguistic cues for fake news: a societal artificial intelligence problem. ArXiv. Preprint ArXiv:2211.14505.

Bagate, R. A., & Suguna, R. (2021). Sarcasm detection of tweets without\# sarcasm: data science approach. Indonesian Journal of Electrical Engineering and Computer Science, 23(2), 993–1001.

Barhoom, A., Abu-Nasser, B. S., & Abu-Naser, S. S. (2022). Sarcasm Detection in Headline News using Machine and Deep Learning Algorithms. International Journal of Engineering and Information Systems (IJEALIS), 6(4), 66–73.

Bharti, S. K., Pradhan, R., Babu, K. S., & Jena, S. K. (2017). Sarcasm analysis on twitter data using machine learning approaches. Trends in Social Network Analysis: Information Propagation, User Behavior Modeling, Forecasting, and Vulnerability Assessment, 51–76.

Bharti, S. K., Sathya Babu, K., & Jena, S. K. (2017). Harnessing online news for sarcasm detection in hindi tweets. International Conference on Pattern Recognition and Machine Intelligence, 679–686.

Chaudhari, P., & Chandankhede, C. (2017). Literature survey of sarcasm detection. 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2041–2046.

Chudi-Iwueze, O., & Afli, H. (2020). Detecting Sarcasm in News Headlines. CERC, 100–111.

Farha, I. A., & Magdy, W. (2020). From arabic sentiment analysis to sarcasm detection: The arsarcasm dataset. Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection, 32–39.

Goel, P., Jain, R., Nayyar, A., Singhal, S., & Srivastava, M. (2022). Sarcasm detection using deep learning and ensemble learning. Multimedia Tools and Applications, 81(30), 43229–43252.

Gul, S., Khan, R. U., Ullah, M., Aftab, R., Waheed, A., & Wu, T. Y. (2022). Tanz-Indicator: A Novel Framework for Detection of Perso-Arabic-Scripted Urdu Sarcastic Opinions. Wireless Communications and Mobile Computing, 2022.

Husain, F., & Uzuner, O. (2021). Leveraging offensive language for sarcasm and sentiment detection in Arabic. Proceedings of the Sixth Arabic Natural Language Processing Workshop, 364–369.

Jariwala, V. P. (2020). Optimal feature extraction based machine learning approach for sarcasm type detection in news headlines. International Journal of Computer Applications, 975, 8887.

Joshi, A., Bhattacharyya, P., & Carman, M. J. (2017). Automatic sarcasm detection: A survey. ACM Computing Surveys (CSUR), 50(5), 1–22.

Kanakam, R., Mohmmad, S., Sudarshan, E., Shabana, S., & Gopal, M. (2022). A survey on approaches and issues for detecting sarcasm on social media tweets. AIP Conference Proceedings, 2418(1).

Katyayan, P., & Joshi, N. (2019). Sarcasm Detection approaches for English language. Smart Techniques for a Smarter Planet: Towards Smarter Algorithms, 167–183.

Kumar, A., & Katiyar, V. (2019). A comparative analysis of sarcasm detection. Int J Recent Eng Res Dev (IJRERD), 4(08), 104–108.

Kumar, A., Narapareddy, V. T., Srikanth, V. A., Malapati, A., & Neti, L. B. M. (2020). Sarcasm detection using multi-head attention based bidirectional LSTM. Ieee Access, 8, 6388–6397.

Kumar, A., Sangwan, S. R., Arora, A., Nayyar, A., Abdel-Basset, M., & others. (2019). Sarcasm detection using soft attention-based bidirectional long short-term memory model with convolution network. IEEE Access, 7, 23319–23328.

Liu, L., Priestley, J. L., Zhou, Y., Ray, H. E., & Han, M. (2019). A2text-net: A novel deep neural network for sarcasm detection. 2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI), 118–126.

Majumder, N., Poria, S., Peng, H., Chhaya, N., Cambria, E., & Gelbukh, A. (2019). Sentiment and sarcasm classification with multitask learning. IEEE Intelligent Systems, 34(3), 38–43.

Mandal, P. K., & Mahto, R. (2019). Deep CNN-LSTM with word embeddings for news headline sarcasm detection. 16th International Conference on Information Technology-New Generations (ITNG 2019), 495–498.

Misra, R. (2022). News headlines dataset for sarcasm detection. ArXiv Preprint ArXiv:2212.06035.

Mohammed, P., Eid, Y., Badawy, M., & Hassan, A. (2020). Evaluation of different sarcasm detection models for arabic news headlines. Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019, 418–426.

Mykytiuk, A., Vysotska, V., Markiv, O., Chyrun, L., & Pelekh, Y. (2023). Technology of Fake News Recognition Based on Machine Learning Methods.

Nayak, D. K., & Bolla, B. K. (2022). Efficient deep learning methods for sarcasm detection of news headlines. In Machine Learning and Autonomous Systems: Proceedings of ICMLAS 2021, 371–382. Springer.

Nguyen, H., Veluchamy, A., Diop, M., & Iqbal, R. (2018). Comparative study of sentiment analysis with product reviews using machine learning and lexicon-based approaches. SMU Data Science Review, 1(4), 7.

Novic, L. I. (2022). A machine learning approach to text-based sarcasm detection [Master theses, City University of New York]. CUNY Academic Works.

Onan, A., & Tocoglu, M. A. (2020). Satire identification in Turkish news articles based on ensemble of classifiers. Turkish Journal of Electrical Engineering and Computer Sciences, 28(2), 1086–1106.

Pal, M., & Prasad, R. (2023). Sarcasm Detection followed by Sentiment Analysis for Bengali Language: Neural Network \& Supervised Approach. 2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS), 1–7.

Park, M., & Chai, S. (2023). Constructing a User-Centered Fake News Detection Model by Using Classification Algorithms in Machine Learning Techniques (Jan 2023). IEEE Access.

Parkar, A., & Bhalla, R. (2023). Analytical comparison on detection of Sarcasm using machine learning and deep learning techniques. International Journal of Computing and Digital Systems, 14(1), 1615–1625.

Pawar, N., & Bhingarkar, S. (2020). Machine learning based sarcasm detection on Twitter data. 2020 5th International Conference on Communication and Electronics Systems (ICCES), 957–961.

Pelser, D., & Murrell, H. (2019). Deep and Dense Sarcasm Detection.

Razali, M. S., Halin, A. A., Ye, L., Doraisamy, S., & Norowi, N. M. (2021). Sarcasm detection using deep learning with contextual features. IEEE Access, 9, 68609–68618.

Sentamilselvan, K., Suresh, P., Kamalam, G. K., Mahendran, S., & Aneri, D. (2021). Detection on sarcasm using machine learning classifiers and rule based approach. IOP Conference Series: Materials Science and Engineering, 1055(1), 12105.

Thavareesan, S., & Mahesan, S. (2019). Sentiment analysis in Tamil texts: A study on machine learning techniques and feature representation. 2019 14th Conference on Industrial and Information Systems (ICIIS), 320–325.

Trystan, S., Matiushchenko, O., & Naumenko, M. (2021). Method Of Recognition Sarcasm In English Communication With The Application Of Information Technologies. CEUR, 3200.

Verma, P., Shukla, N., & Shukla, A. P. (2021). Techniques of sarcasm detection: A review. 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 968–972.

Yin, C., Chen, Y., & Zuo, W. (2021). Multi-task deep neural networks for joint sarcasm detection and sentiment analysis. Pattern Recognition and Image Analysis, 31, 103–108.

Ying, Y., Mursitama, T. N., & others. (2021). Effectiveness of the News Text Classification Test Using the Naïve Bayes' Classification Text Mining Method. Journal of Physics: Conference Series, 1764(1), 12105.
Singh, N., & Jaiswal, U. C. (2024). Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 13(1), e31601.


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