A Multimodal Sentiment Analysis Model Enhanced with Non-verbal Information and Contrastive Learning

LIU Jia; SONG Hong; CHEN Dapeng; WANG Bin; ZHANG Zengwei

doi:10.11999/JEIT231274

Volume 46 Issue 8

Aug. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(8): 3372-3381

LIU Jia, SONG Hong, CHEN Dapeng, WANG Bin, ZHANG Zengwei. A Multimodal Sentiment Analysis Model Enhanced with Non-verbal Information and Contrastive Learning[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3372-3381. doi: 10.11999/JEIT231274

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LIU Jia, SONG Hong, CHEN Dapeng, WANG Bin, ZHANG Zengwei. A Multimodal Sentiment Analysis Model Enhanced with Non-verbal Information and Contrastive Learning[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3372-3381. doi: 10.11999/JEIT231274

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A Multimodal Sentiment Analysis Model Enhanced with Non-verbal Information and Contrastive Learning

doi: 10.11999/JEIT231274 cstr: 32379.14.JEIT231274

LIU Jia^{1, 2, 3, 4},
SONG Hong^{1, 2},
CHEN Dapeng^{1, 2, 3, 4
,
,},
WANG Bin^{1, 2},
ZHANG Zengwei^{1, 2}

1.
Tianchang Research Institute, Nanjing University of Information Science & Technology, Chuzhou 239300, China
2.
School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China
3.
Jiangsu Province Engineering Research Center of Intelligent Meteorological Exploration Robot, Nanjing 210044, China
4.
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing 210044, China

Funds: The National Natural Science Foundation of China (61773219, 62003169), The Key R&D Program of Jiangsu Province (Industry Prospects and Key Core Technologies) (BE2020006-2), The Natural Science Foundation of Jiangsu Province (BK20200823)

Received Date: 2023-11-17
Rev Recd Date: 2024-03-24

Available Online: 2024-04-07

Publish Date: 2024-08-10

Abstract

Abstract

Deep learning methods have gained popularity in multimodal sentiment analysis due to their impressive representation and fusion capabilities in recent years. Existing studies often analyze the emotions of individuals using multimodal information such as text, facial expressions, and speech intonation, primarily employing complex fusion methods. However, existing models inadequately consider the dynamic changes in emotions over long time sequences, resulting in suboptimal performance in sentiment analysis. In response to this issue, a Multimodal Sentiment Analysis Model Enhanced with Non-verbal Information and Contrastive Learning is proposed in this paper. Firstly, the paper employs long-term textual information to enable the model to learn dynamic changes in audio and video across extended time sequences. Subsequently, a gating mechanism is employed to eliminate redundant information and semantic ambiguity between modalities. Finally, contrastive learning is applied to strengthen the interaction between modalities, enhancing the model’s generalization. Experimental results demonstrate that on the CMU-MOSI dataset, the model improves the Pearson Correlation coefficient (Corr) and F1 score by 3.7% and 2.1%, respectively. On the CMU-MOSEI dataset, the model increases “Corr” and “F1 score” by 1.4% and 1.1%, respectively. Therefore, the proposed model effectively utilizes intermodal interaction information while eliminating information redundancy.
- Multimodal emotion analysis,
- Multimodal fusion,
- Information enhancement,
- MultiLayer Perceptron (MLP)

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References(33)

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