ENHANCED SENTIMENT ANALYSIS AND DATA MINING OF POLITICAL LEADERS' POPULARITY ON SOCIAL MEDIA PLATFORMS USING AN OPTIMIZED APACHE HADOOP FRAMEWORK FOR ACCURATE ELECTION OUTCOME PREDICTION

The paper presents an enhanced approach to sentiment analysis and data mining for evaluating the Popularity of political leaders on social media using the Apache Hadoop framework. Social media platforms have become influential in shaping public opinion, making it critical for political campaigns to understand the sentiment behind public discourse. In this study, social media data (e.g., tweets and posts) were collected and processed using Hadoop’s MapReduce framework to efficient handling large-scale data. Sentiment analysis was performed using a logistic regression model to classify public sentiment as positive, negative, or neutral. The model achieved an accuracy of 85%, with a precision of 0.86 for predicting a win and 0.84 for predicting a loss. Positive sentiment drivers such as "Viksit Bharat" and "stronger nation" had a strong positive impact on the likelihood of winning, while terms like "vote" and "voice" were associated with negative sentiment and a higher probability of losing. The study demonstrates that data-driven sentiment analysis can provide valuable insights for political strategists, enabling informed decision-making and improving campaign effectiveness.