ELECTION RESULT PREDICTION

In today’s digitally connected world, social media platforms have become vital spaces for public discourse, especially during elections. Traditional election forecasting methods like opinion polls and surveys are often constrained by time, sample size, and inherent biases. This project introduces a modern, data-driven approach for predicting election outcomes by leveraging Twitter sentiment analysis and Machine Learning (ML) algorithms. The system collects real-time tweets related to political entities using the Twitter API, pre- processes the text data, and classifies public sentiment as positive, negative, or neutral using Natural Language Processing (NLP) tools like VADER and BERT. Additionally, engagement metrics such as likes, retweets, and user influence are extracted to enhance predictive power. The processed data is used to train ML models such as Logistic Regression, Random Forest, and XGBoost, while time-series forecasting techniques like ARIMA and Facebook Prophet are utilized to track sentiment trends over time. The proposed solution offers a scalable and dynamic framework for election prediction, delivering real-time insights and visualizations through an interactive dashboard. This innovative approach enhances the accuracy and timeliness of election forecasting, helping political analysts, campaign strategists, and researchers make data-informed decisions.