Sentiment Analysis Project Overview This sentiment analysis project aims to analyze and classify the emotions expressed in text data. By leveraging natural language processing techniques, the project seeks to categorize text into positive, negative, or neutral sentiments based on the language used.

Key Features Text preprocessing: Includes tasks such as tokenization, stop-word removal, and stemming to clean the text data. Sentiment classification: Utilizes machine learning algorithms like Naive Bayes, Support Vector Machines, or deep learning models like LSTM for sentiment classification. Performance evaluation: Measures the accuracy, precision, recall, and F1 score of the sentiment analysis model to assess its effectiveness.