Test-Time Adaptation and Domain Generalization in Multimodal Sentiment Analysis: From Laboratory to Real-World Deployment

The deployment of multimodal sentiment analysis (MSA) systems in real-world environments is hindered by domain shift, where performance degrades significantly when test data diverges from the training distribution. This review paper provides a comprehensive survey of two pivotal strategies to address this challenge: domain generalization (DG) and test-time adaptation(TTA). We present a novel taxonomy of state-of-the-art methods, categorizing them into data manipulation, representation learning, meta-learning, and hybrid approaches. The review critically examines techniques specifically designed for multimodal data, including modality-specific adaptation and cross-modal consistency enforcement. Furthermore, we discuss the common benchmarks and evaluation metrics that are used to assess robustness to real-world challenges such as missing modalities, not independent and identically distributed (non-i.i.d.) data streams, and computational constraints. Finally, the paper outlines critical open challenges and future research directions, emphasizing the need to handle mixed and continual shifts, adapt large multimodal models efficiently, and improve explainability. This work assists researchers in building reliable MSA systems for real-world deployment.