In this project, I analyzed text data from the 2024 Presidential Debate to uncover sentiment, emotional trends, and discussion topics. Using tools like R, tidytext, and syuzhet, I performed text preprocessing, sentiment analysis (Bing and NRC lexicons), and topic modeling with LDA to identify key themes. Visualizations with ggplot2 highlighted word frequencies, normalized sentiment scores, and emotional distributions. Word clouds showcased distinct messaging styles between the candidates, while topic analysis revealed focus areas such as leadership, policy, and economic issues.

This project honed my ability to transform unstructured text into actionable insights through data cleaning, exploratory analysis, and advanced visualization techniques.