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The challenge of detecting sarcasm in textual content is a fascinating and complex problem in the realm of Natural Language Processing (NLP). Our project aims to tackle this issue by focusing on sarcasm detection in news headlines. Sarcasm detection is crucial because it helps in understanding the underlying sentiment and tone in written language, which can significantly differ from the literal meaning of the words used. In addition to sarcasm detection, our project explores the application of text summarization techniques to distill key information from sarcastic news articles. This comprehensive analysis involves utilizing various NLP techniques and models to distinguish between sarcastic and non-sarcastic news headlines, providing a holistic approach to understanding and processing sarcastic textual content.

The dataset is obtained from Kaggle. Link: https://www.kaggle.com/datasets/rmisra/news-headlines-dataset-for-sarcasm-detection/data