Narrative and Emotion Appeals in Political Speech

• In Event: 40420 - Section on Political Sociology Roundtables
  In Refereed Roundtable (60 minute): Table 9: Speech in Politics

Abstract

This study examines how the social backgrounds of presidential candidates Kamala Harris and Donald Trump influence their emotional appeals and narrative strategies in the 2024 U.S. presidential election. Using Lexicon-based Sentiment Analysis (NLP) and Latent Dirichlet Allocation (LDA), it analyzes their speech rhetoric, emotional intensity, and speech framework. The purpose of this study is to examine the differences in emotional appeals and narrative strategies between the speeches of the two candidates in the 2024 presidential election. It reveals the effects of education, party affiliation, gender and ideological and political behavior. The results suggest that a candidate's political party affiliation frames the target group of policy content and rhetoric, and that educational qualifications, economic class, and career trajectory can lay the groundwork for the linguistic and affective expressions of speeches. This matters because political speeches are not merely communicative acts, but also performative vehicles that shape ideological narratives, mobilize emotions, and reinforce or challenge existing power structures. By revealing how social background factors influence politicians' narrative language and emotional appeals, this study highlights the central role of affective politics in electoral contests and further explicates how language and emotions are necessary in triggering or inhibiting modern political conflict, as well as inducing social mobilization or polarizing voters during election processes. This study contributes to the understanding of how ascribed and attained attributes represented by education, party affiliation, class of origin, and gender shape political discourse, and provides insights into how candidates construct identities, mobilize voters, and reorganize social ideologies.

Authors

• Xinshi Wang, Senshu University

• Xinyue Ma, Senshu University