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U.S. Presidential Election forecasting has been a research interest forseveral decades. Currently, election prediction consists of two mainapproaches: traditional models that incorporate economic data and poll surveys,and models that leverage Twitter (or X) and other social media platforms due totheir increasing popularity in the past decade. However, traditional approacheshave predominantly focused on national-level predictions, while socialmedia-based approaches often oversimplify the nuanced differences betweenonline discourse and the broader voting population's political landscape.  In this work, we perform a hybrid method of both the machine learningalgorithm and the sentiment analysis on the state level with variousindependent variables including census data, economic indicators, pollingaverages, and the newly defined average sentiment scores from Twitter. Ourprediction for the 2020 U.S. Presidential Election yielded promising results.Most of our models successfully predicted a victory for the Democraticcandidate with 96% accuracy using Gradient Boosting Trees and Multi-LayerPerceptron algorithms. This novel prediction framework addresses thelimitations of existing U.S. Presidential Election forecasting approaches,particularly in terms of state-level predictions. It provides a valuablefoundation for future research in this field and contributes to advancing ourunderstanding of election dynamics.

A Hybrid Method of Sentiment Analysis and Machine Learning Algorithm for the U.S. Presidential Election Forecasting