The field of sentiment analysis and multimodal review helpfulness prediction is moving towards more innovative and effective methods for handling complex tasks such as multi-domain and multilingual analysis. Recent developments have focused on improving the accuracy and scalability of existing models, particularly in low-resource languages. Notable advancements include the introduction of novel methodologies for isolating conflicting sentiments and aggregating them to effectively predict the overall sentiment of passages. Additionally, dynamic domain information modulation algorithms have been proposed to efficiently generate domain information required for sentiment classification in each domain. The use of large-scale benchmark datasets, such as those for multimodal review helpfulness prediction, has also become increasingly important for training and evaluating models. Furthermore, unsupervised frameworks for multi-aspect labeling of multilingual and multi-domain review data have shown promise in overcoming the limitations of supervised methods. The development of aspect-based sentiment analysis systems using large language models has also been explored, with a focus on predicting aspect-based opinion quadruples across different domains and languages. Some noteworthy papers in this area include:

• A paper that introduces a novel methodology for isolating conflicting sentiments and aggregating them to predict the overall sentiment of passages, which outperforms baseline models across various datasets.
• A paper that proposes a dynamic domain information modulation algorithm for multi-domain sentiment analysis, which efficiently generates domain information required for sentiment classification in each domain.
• A paper that introduces a large-scale benchmark dataset for multimodal review helpfulness prediction in Vietnamese, which covers four domains and includes 46K reviews.
• A paper that proposes a scalable unsupervised framework for multi-aspect labeling of multilingual and multi-domain review data, which achieves high performance and demonstrates the potential of a robust multi-aspect labeling approach.
• A paper that explores the design of an aspect-based sentiment analysis system using large language models for real-world use, which demonstrates that a combined multi-domain model can effectively handle multiple domain-specific taxonomies simultaneously.