This research presents a machine learning–based framework for predicting future election outcomes by integrating historical election data with sentiment analysis derived from political text and social media discussions. The proposed system combines Natural Language Processing (NLP) techniques such as VADER and TextBlob with a Random Forest classification model to analyze voter sentiment and political trends.The dataset used in this study includes multiple factors influencing election outcomes, such as polling percentages, voter demographics, campaign spending, economic indicators, and sentiment scores extracted from textual political data. These features are processed through a structured machine learning pipeline involving data preprocessing, feature engineering, sentiment extraction, and predictive modeling.Experimental evaluation demonstrates that incorporating sentiment analysis significantly improves prediction accuracy compared with traditional polling-based models. Visualization techniques including confusion matrices, feature importance graphs, and prediction comparison plots are used to interpret model performance and identify influential election factors.The proposed framework provides an effective data-driven approach for political analysis and election forecasting. It can assist political analysts, researchers, and policymakers in understanding voter behavior and predicting election trends using machine learning and sentiment analytics.
1 et al. (Mon,) studied this question.