INTERNATIONAL JOURNAL OF ADVANCE RESEARCH
IN MULTIDISCIPLINARY

This project presents Hashmeter, a real-time Social Media Sentiment Analysis Tool designed to evaluate public sentiment on Twitter. Users input a hashtag, and the system fetches high-engagement tweets using a third-party API. Sentiment analysis is performed using a hybrid model of VADER, TextBlob, BERT, and SpaCy to classify tweets as positive, negative, or neutral.

The frontend, built with React, provides a responsive interface where users can view individual tweet sentiment and an overall sentiment distribution through a visual bar chart. The backend, developed using Flask, handles API requests, tweet processing, and real-time sentiment computation. This integration allows users to monitor public opinion quickly and intuitively. By combining detailed tweet-level analysis with visualized sentiment trends, Hashmeter supports use cases such as brand monitoring, campaign feedback, and social research, offering actionable insights into real-time social media dynamics.