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@article{193736,
        author = {Mrs S Prathima and A Samattha and T Nandu and M Saravana and N Rakesh},
        title = {RECOGNITION OF SARCASM IN NEWS HEADLINES USING ADVANCED LEARNING TECHNIQUES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {1770-1777},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193736},
        abstract = {Sarcasm auto has its significance in application to sentiment analysis and opinion mining, yet it is not clear in the context of news headlines since they are short and they do not give much information. The paper deals with the problem of sarcasm in news headlines, which is detected by a mixture of deep learning and machine learning techniques. The hybrid CNN and BiLSTM model is used to extract meaningful textual features, with CNN identifying important patterns of words and the BiLSTM learning information in a both directions. In conjunction with this, an ensemble model which combines both XGBoost and Support Vector Machine (SVM) is developed that can be trusted in the classification of sarcastic and non-sarcastic headlines. The combination of these models is better in terms of the overall feature representation and language understanding. The system is more stable in integrating the various learning strategies. The final model is XGBoost and SVM ensemble model based on stable performance and strength. The suggested solution also increases the capability of the system to deal with various writing styles and structure of the headlines and makes the process of detecting sarcasm in short news headlines more precise and reliable.},
        keywords = {Sarcasm Detection, News Headlines, Machine Learning, Deep Learning},
        month = {March},
        }