Joey

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.

The paper introduces a method to extract opinion words and their corresponding targets in text using a DOUBLE PROPAGATION technique. This approach is based on the mutual relationship between opinion words (words expressing sentiment) and their targets (entities or features being evaluated). The double propagation method works iteratively, identifying opinion words using known targets and vice versa, expanding both sets iteratively through linguistic patterns and dependency relations in sentences. This semi-supervised approach allows the system to efficiently expand opinion word lexicons and extract sentiment targets with minimal manual intervention, enhancing the accuracy of sentiment analysis in various domains.