EARLY PREDICTION OF PUBLIC OPINION TRENDS IN THE 2024 U.S. PRESIDENTIAL ELECTION USING TOPIC MODELING, DENDROGRAM CLUSTERING, AND SENTIMENT ANALYSIS

Abstract

Traditional forecasting techniques include polls, focus groups and media commentary slow, expensive and unable to accurately determine what the average voter is really thinking. Twitter (now X) offers something different an enormous, real-time record of political opinion written spontaneously by millions of ordinary people, in their own words, without any filter. This thesis explores whether that stream of thought, specifically conversations on Twitter between May and July 2024, three months prior to the US Presidential Election, can provide an advance look at public sentiment. Two entirely independent methods were applied to a dataset of approximately 50,000 tweets drawn from that window. First, Latent Dirichlet Allocation was used to extract underlying themes from the corpus. Three topics emerged, with the one centred on Donald Trump and the MAGA movement proving the most coherent and internally consistent. Hierarchical clustering confirmed this distinctiveness, with "MAGA" forming its own separate cluster sitting apart even from closely associated terms like "GOP" and "republican". The second method analyzed sentiment using four lexicon-based tools: VADER, AFINN, TextBlob and SentiWordNet. Tweets mentioning Trump and tweets mentioning Biden were scored separately then normalized for fair comparison. Across all four tools, the data consistently showed a more positive tone in Trump-related tweets than in Biden-related ones. Critically, these two analyses never interacted in any way or shared information yet they arrived at the same conclusion. Well ahead of polling day, Twitter discourse surrounding Trump demonstrated both greater thematic coherence and a more favorable emotional tone than discourse surrounding Biden, and this independent convergence represents the central finding of this thesis.