Sentiment analysis

Developed a Sentiment Analysis application using Python and Natural Language Processing (NLP) to automatically classify text into positive, negative, or neutral sentiments. The system analyzes customer reviews and social media comments by performing data cleaning, tokenization, stop-word removal, and feature extraction before training and evaluating multiple Machine Learning and Deep Learning models. Implemented and compared models including Logistic Regression, BERT, CNN, LSTM, RNN, and GNN using Python, and evaluated their performance using Accuracy, Precision, Recall, and F1-Score to determine the best classification model.