Advances in Deep Learning for Affective Intelligence: Language Models, Multimodal Trends, and Research Frontiers

The accelerated growth of digital content and the increasing presence of emotional expressions, polarized opinions, and toxic behaviors in social media have driven the development of advanced Affective Analysis techniques. This study presents a broad and up-to-date review of recent studies covering Sentiment Analysis, Emotion Recognition, Hate Speech Detection, cyberbullying, and multimodal approaches grounded in deep learning. The review provides a comparative analysis of the architectures employed—including Transformer-based Models, multimodal frameworks, and variants designed for low-resource languages—along with their metrics, performance outcomes, and emerging patterns. The findings reveal a clear consolidation of Transformer-based Models as the dominant standard, significant progress in multimodality for affective interpretation, and growing attention to multilingual models adapted to diverse cultural contexts. Furthermore, persistent challenges are identified, including limitations related to data availability and quality, Explainable AI (XAI), computational efficiency, and robustness in cross-domain generalization. This review synthesizes current trends, limitations, and opportunities in the field, offering a structured perspective that can serve as a reference for researchers and practitioners involved in the development of more accurate, efficient, and culturally responsible affective systems.