Sentiment analysis, a challenging task in understanding human emotions expressed through diverse modalities, prompts the development of innovative solutions. Multimodal data often contains important complementary information. Effective fusion and extraction of multimodal data features are key issues in sentiment analysis. In this paper, we introduce a novel sentiment analysis model that integrates multimodal multiscale features based on a fuzzy-deep neural network. First, we combine multimodal data, namely text, audio, and images, to extract intrinsic feature representations. Second, our model incorporates the fuzzy-deep neural network learning module, infused with fuzzy logic principles to enhance adaptability to the inherent vagueness in sentiment expressions. Furthermore, we integrate the dual attention mechanism that dynamically focuses on pivotal aspects within multimodal data, refining feature extraction for heightened context-awareness. Rigorous validation across three datasets, including the Multimodal Corpus of Sentiment Intensity dataset, the Multimodal Opinion Sentiment and Emotion Intensity dataset, and the Chinese Single and Multimodal Sentiment dataset, demonstrates the model's superior performance in capturing the intricacies of human emotions.

中文翻译：

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使用模糊深度神经网络学习探索用于情感分析的多模态多尺度特征


情感分析是理解通过不同方式表达的人类情感的一项具有挑战性的任务，它促进了创新解决方案的开发。多模态数据通常包含重要的补充信息。多模态数据特征的有效融合和提取是情感分析的关键问题。在本文中，我们介绍了一种新颖的情感分析模型，该模型基于模糊深度神经网络集成了多模态多尺度特征。首先，我们结合多模态数据，即文本、音频和图像，来提取内在特征表示。其次，我们的模型结合了模糊深度神经网络学习模块，注入模糊逻辑原理，以增强对情感表达固有模糊性的适应性。此外，我们集成了双重注意力机制，动态关注多模态数据中的关键方面，细化特征提取以增强上下文感知。对三个数据集（包括多模态情感强度数据集、多模态观点情感和情感强度数据集以及中文单模态和多模态情感数据集）的严格验证证明了该模型在捕捉人类情感的复杂性方面的卓越性能。