Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis

Citation

Poria S, Peng H, Hussain A, Howard N & Cambria E (2017) Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis. Neurocomputing, 261, pp. 217-230. https://doi.org/10.1016/j.neucom.2016.09.117

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.

Keywords
Multimodal sentiment analysis; Convolutional neural network; Deep learning; Sentiment; Emotion; MKL; ELM; SVM; Classification

Journal
Neurocomputing: Volume 261