Analyzing Multimodal Sentiment Via Acoustic- and Visual-LSTM With Channel-Aware Temporal Convolution Network
Abstract
The emotion of human is always expressed in a multimodal perspective. Analyzing multimodal human sentiment remains challenging due to the difficulties of the interpretation in inter-modality dynamics. Mainstream multimodal learning architectures tend to design various fusion strategies to learn inter-modality interactions, which barely consider the fact that the language modality is far more important than the acoustic and visual modalities. In contrast, we learn inter-modality dynamics in a different perspective via acoustic- and visual-LSTMs where language features play dominant role. Specifically, inside each LSTM variant, a well-designed gating mechanism is introduced to enhance the language representation via the corresponding auxiliary modality. Furthermore, in the unimodal representation learning stage, instead of using RNNs, we introduce `channel-aware' temporal convolution network to extract high-level representations for each modality to explore both temporal and channel-wise interdependencies. Extensive experiments demonstrate that our approach achieves very competitive performance compared to the state-of-the-art methods on three widely-used benchmarks for multimodal sentiment analysis and emotion recognition.
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Nonlexical modalities often contain noise that may interfere with the learning of cross-modal interactions and influence the quality of the multimodal representation.
“These nonlexical modalities often contain noise that may interfere with the learning of cross-modal interactions and influence the quality of the multimodal representation.”
From Multimodal Information Bottleneck: Learning Minimal Sufficient Unimodal and Multimodal Representations (2023) · cites this paper as [3]
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Self-citation
- Authors
- Mai, S., Xing, S., & Hu, H.
- Affiliations
- Sun Yat-sen University (CN)
- Venue
- IEEE/ACM Transactions on Audio, Speech, and Language Processing
- Published
- 2021
- Volume
- 29
- Pages
- 1424-1437
- ISSN
- 2329-9290 (ISSN-L)
- 2329-9304
- OpenAlex
- W3141688548
- Cited by
- 89
- References
- 14
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 19 · 2023: 20 · 2022: 21 · 2021: 5
- FWCI
- 5.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE/ACM Transactions on Audio Speech and Language Processing (publishedVersion)
- Funders
- National Key Research and Development Program of China (2018YFB1601101, 2018YFB1601100)
- National Natural Science Foundation of China (62076262)
- Natural Science Foundation of Guangdong Province (2017A030311029)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- multimodal sentiment analysis, emotion recognition, gating mechanism, temporal representation learning
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