非语言信息增强和对比学习的多模态情感分析模型

刘佳; 宋泓; 陈大鹏; 王斌; 张增伟

doi:10.11999/JEIT231274

非语言信息增强和对比学习的多模态情感分析模型

doi: 10.11999/JEIT231274

刘佳^{1, 2, 3, 4},
宋泓^{1, 2},
陈大鹏^{1, 2, 3, 4, ,},
王斌^{1, 2},
张增伟^{1, 2}

1.
南京信息工程大学天长研究院滁州 239356
2.
南京信息工程大学自动化学院南京 210044
3.
江苏省智能气象探测机器人工程研究中心南京 210044
4.
江苏省大气环境与装备技术协同创新中心南京 210044

基金项目: 国家自然科学基金(61773219, 62003169)，江苏产业前瞻与关键技术重点项目(BE2020006-2)，江苏省自然科学基金青年基金(BK20200823)

详细信息

作者简介:
刘佳：女，教授，研究方向为多模态情感分析、计算机视觉与图像处理、虚拟/增强现实

宋泓：男，硕士生，研究方向为多模态情感分析、计算机视觉与图像处理

陈大鹏：男，副教授，研究方向为计算机视觉与图像处理、虚拟/增强现实、人机交互

王斌：男，硕士生，研究方向为计算机视觉与图像处理、虚拟/增强现实

张增伟：男，硕士生，研究方向为增强现实、人机交互

通讯作者:
陈大鹏　dpchen@nuist.edu.cn

中图分类号: TN911.7; TP391
计量
- 文章访问数: 181
- HTML全文浏览量: 68
- PDF下载量: 17
- 被引次数: 0
出版历程
- 收稿日期: 2023-11-17
- 修回日期: 2024-03-24
- 网络出版日期: 2024-04-07

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

LIU Jia^{1, 2, 3, 4},
SONG Hong^{1, 2},
CHEN Da-Peng^{1, 2, 3, 4
, ,},
WANG Bin^{1, 2},
ZHANG Zeng-Wei^{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)

摘要

摘要: 因具有突出的表征和融合能力，深度学习方法近年来越来越多地被应用于多模态情感分析领域。已有的研究大多利用文字、面部表情、语音语调等多模态信息对人物的情绪进行分析，并主要使用复杂的融合方法。然而，现有模型在长时间序列中未充分考虑情感的动态变化，导致情感分析性能不佳。针对这一问题，该文提出非语言信息增强和对比学习的多模态情感分析网络模型。首先，使用长程文本信息去促使模型学习音频和视频在长时间序列中的动态变化，然后，通过门控机制消除模态间的冗余信息和语义歧义。最后，使用对比学习加强模态间的交互，提升模型的泛化性。实验结果表明，在数据集CMU-MOSI上，该模型将皮尔逊相关系数(Corr)和F1值分别提高了3.7%和2.1%；而在数据集CMU-MOSEI上，该模型将“Corr”和“F1值”分别提高了1.4%和1.1%。因此，该文提出的模型可以有效利用模态间的交互信息，并去除信息冗余。
- 多模态情感分析 /
- 多模态融合 /
- 信息增强 /
- MLP
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 /
- MLP

HTML全文

图 1 不同模态组合的情绪预测

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图 2 模型结构图

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图 3 非语言信息增强机制结构图

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图 4 MLP融合单元

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图 5 各基准模型性能比较

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图 6 各辅助模态性能可视化

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表 1 模型对比实验结果CMU-MOSI和CMU-MOSEI

模型	CMU-MOSI					CMU-MOSEI
模型	MAE	Corr	Acc-7	Acc-2	F1	MAE	Corr	Acc-7	Acc-2	F1
TFN	0.901	0.698	34.90	80.80	80.70	0.593	0.700	50.20	82.50	82.10
LMF	0.917	0.695	33.20	82.50	82.40	0.623	0.677	48.00	82.00	82.10
MULT	0.918	0.680	36.47	79.30	79.34	0.580	0.703	51.80	82.50	82.30
ICCN	0.860	0.710	39.00	83.00	83.00	0.565	0.713	51.60	84.20	84.20
MISA	0.783	0.761	42.30	83.40	83.60	0.555	0.756	52.20	85.50	85.30
MAG-BERT	0.713	0.789	—	84.30	84.30	0.539	0.753	—	85.23	85.08
MGHF	0.709	0.802	45.19	85.21	85.21	0.528	0.767	53.70	85.30	84.86
MHMF-BERT	0.701	0.787	—	85.30	85.30	0.519	0.761	—	85.60	85.60
CENet(B)	0.698	0.806	—	86.74	86.66	0.515	0.816	—	86.24	86.16
Ours	0.633	0.843	48.10	88.73	88.80	0.482	0.830	52.80	87.30	87.30

下载: 导出CSV

表 2 CMU-MOSI和CMU-MOSEI数据集上消融实验研究结果(-G代表移除了NE模块中的门控机制)

模型	CMU-MOSI			CMU-MOSEI
模型	MAE(↓)	Corr(↑)	F1(↑)	MAE(↓)	Corr(↑)	F1(↑)
Base	0.702	0.810	85.32	0.550	0.767	84.92
Base + NE^(A)	0.688	0.812	85.52	0.552	0.772	85.01
Base + NE^(V)	0.674	0.823	85.61	0.542	0.776	85.53
Base + NE^(T)	0.654	0.833	87.06	0.525	0.793	86.03
Base + NE^(T)-G	0.665	0.825	86.60	0.535	0.787	85.60
Base + contrast	0.673	0.824	86.63	0.539	0.790	85.66
Ours	0.633	0.843	88.88	0.482	0.830	87.30

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表 3 (A代表本文提出的模型，B代表去除非语言信息增强模块和对比学习策略的模型)

示例		标签	A	B
T	It’s been a great day.	2.53	2.52	2.05
A	正常、平和的语气
V	微笑
T	He was the only character that slightly interesting.	–0.80	–0.82	–0.10
A	迟疑的语气
V	摇头、眉头紧皱
T	I give Shrek Forever After directed by Mike Mitchell a grade of B minus.	1.00	0.95	–0.98
A	正常的语气
V	面无表情

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