Sarcasm-Detection

This repository presents a machine learning solution for detecting sarcasm in sentences using the CountVectorizer and Naive Bayes classifier. Sarcasm detection is a challenging task in natural language processing due to the nuanced nature of sarcasm and its dependence on context. Leveraging a dataset of labeled sarcastic and non-sarcastic sentences, this project employs the CountVectorizer technique to convert text data into numerical features and trains a Naive Bayes classifier to classify sentences as sarcastic or non-sarcastic based on these features. By analyzing word frequencies and probabilities, the model can effectively distinguish sarcastic utterances from non-sarcastic ones, aiding in sentiment analysis and social media monitoring applications. The repository includes data preprocessing, model training, and evaluation, providing a robust framework for sarcasm detection using machine learning techniques.