Predicting Emotional Intensity in Political Debates via Non-verbal Signals.

Non-verbal expressions of politicians are important in election. In particular, the emotional intensity of politician revealed in a debate can be strongly linked to voters' evaluation. This paper proposes a multimodal deep-learning model for predicting the perceived emotional intensity of a candidate, which utilizes voice, face, and gesture to capture the comprehensive information of one's emotional intensity revealed in a debate. We collect a dataset of political debate videos from the 2020 Democratic presidential primaries in the USA, and train the proposed model with randomly sampled clips from the debate videos. By applying the proposed model to 23 candidates in 11 debate videos, we show that the standard deviation of the perceived emotional intensity is positively correlated with the changes in candidates' favorability in public polls.