A Streamlit-Based Web App for Real-Time Sentiment Analysis and Visualization
DOI:
https://doi.org/10.69996/fmep.2025003Keywords:
Streamlit, Natural Language Processing, Machine Learning, Text MiningAbstract
The Sentiment Analysis App using Streamlit project focuses on the development of an intuitive and efficient tool for analyzing and visualizing sentiments in textual data. Leveraging the capabilities of the Streamlit library, this application provides a user-friendly interface that simplifies the process of sentiment analysis. By incorporating natural language processing algorithms, the app categorizes input text or uploaded datasets into positive, negative, or neutral sentiments. Users can input text directly into the application or upload datasets in various formats, ensuring flexibility and ease of use. The app's dynamic visualizations, such as pie charts, bar graphs, and word clouds, offer real-time insights into
sentiment distributions. Additionally, the application allows users to explore sentiment trends over time, enabling a deeper understanding of how sentiments evolve. With customization options and the ability to
export results, the Sentiment Analysis App caters to diverse user preferences and analytical needs. Furthermore, the integration with external APIs enhances the app's capabilities, enabling users to benefit
from advanced sentiment analysis models and stay abreast of developments in natural language processing. Overall, this project aims to democratize sentiment analysis, making it accessible and valuable across various domains, including social media monitoring, customer feedback analysis, and content evaluation
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