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
Copyright Status
Unknown
Socpus ID
85016626046 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85016626046
STARS Citation
Nojavanasghari, Behnaz; Gopinath, Deepak; Koushik, Jayanth; Baltrušaitis, Tadas; and Morency, Louis Philippe, "Deep Multimodal Fusion For Persuasiveness Prediction" (2016). Scopus Export 2015-2019. 4364.
https://stars.library.ucf.edu/scopus2015/4364