Analyzing Sentiments: A Comprehensive Study of Roberta-Based Sentiment Analysis on Twitters

Social media platforms have become essential avenues for individuals to express opinions and emotions across diverse topics such as products, events, and policies, using both text and emojis. Understanding these sentiments is crucial, yet manually analyzing the vast data on platforms like Twitter is impractical. Automated sentiment analysis tools, exemplified by Roberta, offer a solution. Roberta, an evolving language model, employs advanced bidirectional learning to predict sentiment labels accurately. This study focuses on sentiment analysis on Twitter, leveraging Roberta to autonomously categorize tweets into positive, negative, or neutral sentiments. We utilizing natural language processing and machine learning algorithms for dataset preprocessing, incorporating sources like Kaggle and ensuring data integrity. Assessment metrics encompassing accuracy, precision, recall, and F1-score, in addition to confusion metrics, assess model performance. According to our findings, machine learning powered sentiment analysis models are dependable, even in the face of challenges and more so when processing Twitter data sets. The study gives direction on how this kind of model can be of help in analyzing sentiments expressed through social networking sites thus assisting scholars and professional Consultancy in the field.