利用单模态标签生成和模态分解进行多模态情感分析

IF 14.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Linan Zhu , Hongyan Zhao , Zhechao Zhu , Chenwei Zhang , Xiangjie Kong
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引用次数: 0

摘要

多模态情感分析旨在结合来自不同模态的信息,加强对情感的理解并实现准确预测。然而,现有方法在融合过程中面临信息冗余和模态异构的问题,而且常见的多模态情感分析数据集缺乏单模态标签。为解决这些问题,本文提出了一种基于单模态标签生成和模态分解(ULMD)的多模态情感分析方法。该方法采用多任务学习框架,将多模态情感分析任务分为一个多模态任务和三个单模态任务。此外,还引入了模态表征分离器,将模态表征分解为模态不变表征和特定模态表征。这种方法探索了模态之间的融合,并生成了单模态标签,从而提高了多模态情感分析任务的性能。在两个公共基准数据集上进行的广泛实验证明了这种方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimodal sentiment analysis with unimodal label generation and modality decomposition
Multimodal sentiment analysis aims to combine information from different modalities to enhance the understanding of emotions and achieve accurate prediction. However, existing methods face issues of information redundancy and modality heterogeneity during the fusion process, and common multimodal sentiment analysis datasets lack unimodal labels. To address these issues, this paper proposes a multimodal sentiment analysis approach based on unimodal label generation and modality decomposition (ULMD). This method employs a multi-task learning framework, dividing the multimodal sentiment analysis task into a multimodal task and three unimodal tasks. Additionally, a modality representation separator is introduced to decompose modality representations into modality-invariant representations and modality-specific representations. This approach explores the fusion between modalities and generates unimodal labels to enhance the performance of the multimodal sentiment analysis task. Extensive experiments on two public benchmark datasets demonstrate the effectiveness of this method.
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来源期刊
Information Fusion
Information Fusion 工程技术-计算机:理论方法
CiteScore
33.20
自引率
4.30%
发文量
161
审稿时长
7.9 months
期刊介绍: Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.
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