A review on Sentiment Analysis Using Machine Learning Techniques

This project presents a sentiment analysis tool designed to classify Twitter data as positive or negative, leveraging the Multinomial Naïve Bayes model and a TF-IDF vectorizer[2]. Using Python and a Tkinter- based graphical user interface, the application allows users to upload datasets or input custom text for real-time sentiment predictions. The tool processes text data by transforming it into feature vectors, which are then analyzed to determine sentiment polarity[5]. This system aims to aid in understanding public sentiment on social media platforms, providing a user-friendly interface for exploring sentiment trends[12]. The experimental results show promising accuracy, demonstrating the efficacy of Naïve Bayes for social media sentiment classification.