Joey

这篇论文提出了一种基于超图的多模态情绪识别方法，旨在通过超图建立多模态数据之间的多元关系，提升情绪识别的准确性。该模型利用胶囊网络提取模态特征，超图卷积则用于学习模态间的关系，实现特征的有效融合。实验表明，该方法在情绪分类任务中表现优于传统二元关系图模型，特别是在处理未对齐的多模态数据时效果显著提升。

这篇文章总结了多模态情感分析的核心技术，介绍了面部表情、语音、文本等单模态情感识别方法，以及通过模态融合（特征级融合、决策级融合和混合融合）提高情感分析准确性的方法。文章还讨论了常用的数据集和目前面临的挑战，如数据集的局限、模态权重分配问题和算法复杂度。未来研究应着重于更大规模数据集的构建和优化融合算法。

This paper introduces a machine learning framework based on Lexicalized Hidden Markov Models (HMMs) for extracting and analyzing opinions from web product reviews. The framework aims to identify product-related entities (such as features and components) and classify their associated opinions (positive or negative). By integrating part-of-speech tags and contextual information into the HMMs, the model improves its accuracy in identifying both frequent and infrequent opinion phrases. The method also incorporates a bootstrapping process to self-learn new vocabularies, reducing the need for extensive manual labeling. Experimental results show that this framework outperforms rule-based methods in opinion sentence extraction and opinion polarity classification, providing a more robust solution for opinion mining in e-commerce environments.