Research Area: Machine Learning
We tackle the crucial challenge of fusing different modalities of features for multimodal sentiment analysis. Mainly based on neural networks, existing approaches largely model multimodal interactions in an implicit and hard-to-understand manner. We address this limitation with inspirations from quantum theory, which contains principled methods for modeling complicated interactions and correlations. In our quantum-inspired framework, the word interaction within a single modality and the interaction across modalities are formulated with superposition and entanglement respectively at different stages. The complex-valued neural network implementation of the framework achieves comparable results to state-of-the-art systems on two benchmarking video sentiment analysis datasets. In the meantime, we produce the unimodal and bimodal sentiment directly from the model to interpret the entangled decision.
Keywords:
Multimodal sentiment analysis
Quantum theory
Machine learning
Author(s) Name: Qiuchi Li, Dimitris Gkoumas, Christina Lioma, Massimo Melucci
Journal name: Information Fusion
Conferrence name:
Publisher name: Elsevier
DOI: /10.1016/j.inffus.2020.08.006
Volume Information: Volume 65, January 2021, Pages 58-71
