Computational emotion analysis with multimodal LLMs: Current evidence on an emerging methodological opportunity

Emotions are central to politics and analyzing their role in political communication has a long tradition. As research increasingly leverages audio-visual materials to analyze emotions, the emergence of multimodal generative Artificial Intelligence (AI) promises great advances. However, we lack evidence about the effectiveness of multimodal AI in analyzing emotions in political communication. This paper addresses this gap by evaluating current multimodal large language models (mLLMs) in the video-based analysis of emotional arousal, using two complementary datasets of human-labeled video recordings. It finds that under ideal circumstances, mLLMs' emotional arousal ratings are highly reliable and exhibit little to no demographic bias. However, in recordings of real-world parliamentary debates, mLLMs' arousal ratings fail to deliver on this promise with potential negative consequences for downstream statistical inferences. This study therefore underscores the need for continued, thorough evaluation of emerging generative AI methods in multimodal political analysis and contributes a suitable replicable framework.