Understanding public opinion from social media platforms has become increasingly important, and sentiment analysis plays a key role in this process. Although sentiment analysis tools such as VADER and TextBlob are widely used, they often require programming skills and lack accessible interfaces for exploratory analysis. This paper introduces the design and workflow of an interactive web-based application developed to simplify sentiment evaluation of English tweets. The system combines lexicon-based sentiment models with a Streamlit dashboard to offer an easy-to-use platform for analyzing individual texts, processing bulk tweet datasets (CSV files), and visualizing outcomes using dynamic plots and word clouds. By integrating established analysis techniques into an intuitive interface, the application makes sentiment analysis approachable for users without technical backgrounds.
Choudhary et al. (Thu,) studied this question.
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