S. Poria
Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis
Poria, S.; Peng, H.; Hussain, A.; Howard, N.; Cambria, E.
Abstract
The advent of the Social Web has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. In pace with a global deluge of videos from billions of computers, smartphones, tablets, university projectors and security cameras, the amount of multimodal content on the Web has been growing exponentially, and with that comes the need for decoding such information into useful knowledge. In this paper, a multimodal affective data analysis framework is proposed to extract user opinion and emotions from video content. In particular, multiple kernel learning is used to combine visual, audio and textual modalities. The proposed framework outperforms the state-of-the-art model in multimodal sentiment analysis research with a margin of 10–13% and 3–5% accuracy on polarity detection and emotion recognition, respectively. The paper also proposes an extensive study on decision-level fusion.
Journal Article Type | Article |
---|---|
Acceptance Date | Sep 22, 2016 |
Online Publication Date | Feb 8, 2017 |
Publication Date | Oct 25, 2017 |
Deposit Date | Sep 3, 2019 |
Journal | Neurocomputing |
Print ISSN | 0925-2312 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 261 |
Pages | 217-230 |
DOI | https://doi.org/10.1016/j.neucom.2016.09.117 |
Public URL | http://researchrepository.napier.ac.uk/Output/1792476 |
You might also like
Applications of Deep Learning and Reinforcement Learning to Biological Data
(2018)
Journal Article
Guided Policy Search for Sequential Multitask Learning
(2018)
Journal Article
Learning Latent Features With Infinite Nonnegative Binary Matrix Trifactorization
(2018)
Journal Article
Cross-modality interactive attention network for multispectral pedestrian detection
(2018)
Journal Article
Downloadable Citations
About Edinburgh Napier Research Repository
Administrator e-mail: repository@napier.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search