SENTIMENT ANALYSIS USING MACHINE LEARNING

SENTIMENT ANALYSIS USING MACHINE LEARNING

Mr. Ayush1, Mr. Dhruv Sharma2, Mr. Rishi Kumar3, Mr. Hardik Dagar4,Mr. Rohan Sharma5
1 Assistant Professor 2,3,4.5 Student CSE Department

HMR Institute of Technology And Management, GGSIPU, Delhi 110036 Dhruvsharm001@gmail.com

Abstract - 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.

Keywords: Naive Bayes Classifier, TF-IDF Vectorization, Sentiment Analysis, Machine Learning, Real-Time Feedback, Text Pre-Processing, Natural Language Processing (NLP).