Advanced Algorithms in Multimodal Affective Computing

Dear Colleagues, 

The field of Multimodal Affective Computing (MAC) has experienced significant growth, driven by the increasing need to understand and interpret human emotions across various modalities such as speech, text, facial expressions, and physiological signals. As we advance towards more sophisticated computational models, the development of advanced algorithms becomes paramount to enhance the accuracy, reliability, and applicability of MAC systems. This Special Issue, titled “Advanced Algorithms in Multimodal Affective Computing,” aims to bring together the latest research findings, innovative methodologies, and practical applications that push the boundaries of MAC. 

In this context, the Special Issue will focus on the theoretical and practical aspects of developing advanced algorithms for MAC, including, but not limited to, the following: 

• Novel algorithms for emotion recognition and sentiment analysis that leverage multimodal data;
• Integration of large language models, machine learning,and deep learning techniques to improve the performance of MAC systems;
• Exploration of attention mechanisms, transfer learning, and meta-learning in the context of MAC;
• Development of robust and interpretable models that can generalize across different datasets and real-world scenarios;Sentiment Analysis Across Modalities
• Applications of advanced algorithms in diverse domains such as healthcare, education, human–computerinteraction, and customer service;
• Addressing challenges related to data scarcity, noise, and variability in multimodal datasets;
• Innovations in feature extraction and fusion strategies for multimodal data;
• Evaluation methodologies and benchmark datasets for assessing the performance of MAC algorithms;
• Ethical considerations and privacy issues in the development and deployment of MAC systems.

Prof. Dr. Sijie Mai
Guest Editor

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• emotion recognition
• multimodal learning
• affective computing
• deep learning for affect analysis
• sentiment analysis across modalities
• machine learning in human–computer interaction
• emotion understanding systems
• data fusion in affective computing
• real-time emotion inference
• ethical ai in emotional intelligence