Deep Multimodal Fusion for Persuasiveness Prediction

Behnaz Nojavanasghari

Deepak Gopinath

Jayanth Koushik

Tadas Baltrušaitis

Louis-Philippe Morency

Abstract: Persuasiveness is a high-level personality trait that quantifies the influence a speaker has on the beliefs, attitudes, intentions , motivations, and behavior of the audience. With social multimedia becoming an important channel in propagating ideas and opinions, analyzing persuasiveness is very important. In this work, we use the publicly available Persuasive Opinion Multimedia (POM) dataset to study persuasion. One of the challenges associated with this problem is the limited amount of annotated data. To tackle this challenge, we present a deep multimodal fusion architecture which is able to leverage complementary information from individual modalities for predicting persuasiveness. Our methods show significant improvement in performance over previous approaches.


  • Persuasiveness
  • Deep neural networks
  • Multimodal fusion
This publication uses Facial Expression Analysis which is fully integrated into iMotions Lab

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