Title

Deep Multimodal Fusion For Persuasiveness Prediction

Keywords

Deep neural networks; Multimodal fusion; Persuasiveness

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.

Publication Date

10-31-2016

Publication Title

ICMI 2016 - Proceedings of the 18th ACM International Conference on Multimodal Interaction

Number of Pages

284-288

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/2993148.2993176

Socpus ID

85016626046 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85016626046

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