Sentiment Analysis of Twitter Feeds: Comparison of Different Data Driven Techniques

Twitter serves as a dynamic platform for real-time communication, where users share opinions, news, and trends globally. The vast volume of daily tweets provides a rich source for sentiment analysis, enabling organizations to effectively gauge public opinion and consumer sentiment. This data-driven analysis not only supports marketing strategies but also enhances decision-making in areas like politics, public health, and brand management. However, several studies face limitations such as low data quality, contextual challenges, and resource intensive. To address these issues, this study evaluates the performance of three models, Bert, RoBERTa, and DistilBERT- on Twitter sentiment analysis. The models are assessed based on accuracy, precision, recall, and F1-score. Among the models, RoBERTa demonstrates the highest performance for both positive and negative sentiment analysis.