  U.S. Presidential Election forecasting has been a research interest for
several decades. Currently, election prediction consists of two main
approaches: traditional models that incorporate economic data and poll surveys,
and models that leverage Twitter (or X) and other social media platforms due to
their increasing popularity in the past decade. However, traditional approaches
have predominantly focused on national-level predictions, while social
media-based approaches often oversimplify the nuanced differences between
online discourse and the broader voting population's political landscape.
  In this work, we perform a hybrid method of both the machine learning
algorithm and the sentiment analysis on the state level with various
independent variables including census data, economic indicators, polling
averages, and the newly defined average sentiment scores from Twitter. Our
prediction for the 2020 U.S. Presidential Election yielded promising results.
Most of our models successfully predicted a victory for the Democratic
candidate with 96% accuracy using Gradient Boosting Trees and Multi-Layer
Perceptron algorithms. This novel prediction framework addresses the
limitations of existing U.S. Presidential Election forecasting approaches,
particularly in terms of state-level predictions. It provides a valuable
foundation for future research in this field and contributes to advancing our
understanding of election dynamics.
