Using OpenAI models as a new tool for text analysis in political leaders’ unstructured discourse

Abstract

This study explores the application of Large Language Models (LLMs) and Automatic Speech Recognition (ASR) models in the analysis of right-wing unstructured political discourse in Peru, focusing on how the concept of freedom is framed. Three types of freedom are identified: personal autonomy, economic freedom, and civil liberties. Utilizing the transcription of OpenAI’s ASR Whisper and GPT-3.5 and GPT-4 models, interviews with three Peruvian right-wing political leaders are analyzed: Rafael López Aliaga, Hernando de Soto and Keiko Fujimori. The results show that GPT-4 beats GPT-3.5 in identifying dimensions of freedom, although there are discrepancies compared to human coding. Despite challenges in classifying abstract and ambiguous concepts, the findings demonstrate GPT-4's ability to classify complexities within political discourse at comparatively small costs and easy access. The research suggests the need for additional refinement, ethical consideration, and ongoing exploration in the analysis of political speeches through AI.