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Sentiment Analysis on Donald Trump in the 2024 United States Presidential Elections

Abstract:

Donald Trump's victory in the 2024 United States presidential election raises questions about how he managed to secure enough support despite his controversial views. This study addresses this issue through a sentiment analysis applied to data collected from social media and news outlets. The information was obtained through scraping news articles and TikTok posts, utilizing the APIs of the New York Times and Google News, as well as Selenium for web scraping. The sentiment analysis was performed using a fine-tuned BERT-based model to analyze both short and complex linguistic structures. The results indicate that while negative comments about Trump were more frequent, positive sentiments had higher engagement and diffusion, particularly on social media platforms. This suggests that support for Trump was amplified through digital interactions despite prevailing criticism. Moreover, this study highlights how dominant narratives and opinions on digital platforms and traditional media may have influenced public perception, ultimately contributing to the electoral outcome. Given the critical role of public opinion in political decision-making, these findings provide insights into how media framing and online discourse shape electoral dynamics. This research underscores the importance of sentiment analysis in understanding complex sociopolitical phenomena, offering a data-driven perspective on the relationship between digital discourse and political support.