Examining Multimodel Emotion Assessment and Resonance with Audience on YouTube-Reference-Cited by-同舟云学术

Examining Multimodel Emotion Assessment and Resonance with Audience on YouTube

Published:2024-04-20 Issue: Volume:3 Page:85-93
ISSN:
Container-title:Proceedings of the 2024 9th International Conference on Multimedia and Image Processing
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Author:

Yousefi Niloofar¹^ORCID,Cakmak Mert Can¹^ORCID,Agarwal Nitin¹^ORCID

Affiliation:

1. COSMOS Research Center, University of Arkansas at Little Rock, USA

Funder

U.S. Office of the Under Secretary of Defense for Research and Engineering

U.S. Defense Advanced Research Projects Agency

U.S. National Science Foundation

U.S. Office of Naval Research

U.S. Army Research Office

Arkansas Research Alliance, the Jerry L. Maulden Entergy Endowment at the University of Arkansas at Little Rock, and the Australian Department of Defense Strategic Policy Grants Program (SPGP)

U.S. Air Force Research Laboratory

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3665026.3665039

Reference38 articles.

1. Predicting emotions in online social networks: challenges and opportunities

2. Dayo Samuel Banjo, Connice Trimmingham, Niloofar Yousefi, and Nitin Agarwal. 2022. Multimodal Characterization of Emotion within Multimedia Space. International Conference on Computers and Computation (COMPUTE 2022) (2022).

3. A first look into a Convolutional Neural Network for speech emotion detection

4. Human emotion recognition and analysis in response to audio music using brain signals

5. Introduction to the Special Issue on Emotions in Reading, Learning, and Communication

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