Multimodal Sentiment Analysis Using Deep Learning and Metaheuristic Optimization

Multimodal sentiment analysis combines information from different data modalities to identify and interpret opinions, emotions, & attitudes. This book examines deep learning methods for sentiment analysis and the role of metaheuristic optimization in improving computational models. It presents multimodal data processing, feature representation, neural network learning, classification, and optimization. Attention is given to integrating textual and complementary signals for sentiment-oriented analysis and pattern recognition. Deep learning provides foundation for complex representations, while metaheuristic optimization offers strategies for exploring model parameters and improving solution quality. The book brings these methods together within the computational framework relevant to natural language processing, machine learning (ML), artificial intelligence, data mining, and pattern recognition. Students and engineers can use it to study multimodal sentiment analysis and deep learning.