A deep learning framework for tonal trajectory, segment dynamics, and speaker influence in political talk shows

This study introduces a computational framework for analyzing the tonal structure and interactional dynamics of political discussion programs broadcast on Turkish television. Using a self-constructed dataset of televised debates featuring journalists, politicians, and commentators, we trained a CNN on MFCCs to classify speech segments into three tonal categories: sociable, discuss, and monotone. The model achieved strong performance, with an average classification accuracy of 92.3% across five fold cross-validation. To assess the role of individual speakers, we modeled participant influence using Ridge Regression with a CLR transformation, estimating how each participant contributed to the overall tonal composition of episodes. In addition, the study introduces several structural analyses of tonal behavior, including tonal trajectory analysis to observe how tone evolves throughout episodes and segment-to-segment transition modeling to capture sequential tone shifts. Furthermore, entropy-based diversity metrics are employed to assess tonal balance and variety across episodes. Together, these components offer a multi-dimensional view of televised political discourse, revealing both who shapes the tone and how tonal dynamics unfold over time. The findings contribute to computational media studies, political communication, and the analysis of broadcast interaction in polarized environments.