Abstract
This study analyzes the linguistic patterns and rhetorical strategies employed in the 2024 U.S. presidential debates from the exchanges between Donald Trump, Joe Biden, and Kamala Harris. This paper examines debate transcripts to find underlying themes and communication styles using Natural Language Processing (NLP) advanced techniques, including an n-gram analysis, sentiment analysis, and lexical diversity measurements. The methodology combines a quantitative text analysis with qualitative interpretation through the Jaccard similarity coefficient, the Type–Token Ratio, and the Measure of Textual Lexical Diversity. The empirical results reveal distinct linguistic profiles for each candidate: Trump consistently employed emotionally charged language with high sentiment volatility, while Biden and Harris demonstrated more measured approaches with higher lexical diversity. Finally, this research contributes to the understanding of political discourse in high-stakes debates through NLP and can offer information on the evolution of the communication strategies of the presidential candidates of any country with this regime.
Published Version
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