Sarcasm Detection in Text Using Machine Learning Algorithms

This study presents a machine learning approach to detect sarcasm in text data, focusing on social media content. The model analyzes textual features and recognizes sarcastic expressions using techniques like Random Forest, Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks. To improve model accuracy, feature extraction methods such as sentiment analysis and word embeddings are used. With its great performance in sarcasm identification, the suggested method offers a useful tool for applications involving sentiment analysis and natural language processing. The findings show how well the model differentiates between sardonic and non-sarcastic content, providing information about online communication trends.