Year: 2026
Type: article
Abstract: With the rapid development of the Internet, online public opinion monitoring has emerged as a crucial task in the information era. Multimodal sentiment analysis, through the integration of multiple modalities such as text, images, and audio, combined with technologies including natural language processing and computer vision, offers novel technical means for online public opinion monitoring. Nevertheless, current research still faces many challenges, such as the scarcity of high-quality datasets, limited model generalization ability, and difficulties with cross-modal feature fusion. This paper reviews the current research progress of multimodal sentiment analysis in online public opinion monitoring, including its development history, key technologies, and application scenarios. Existing problems are analyzed and future research directions are discussed. In particular, we emphasize a fusion-architecture-centric comparison under online public opinion monitoring, and discuss cross-lingual differences that affect multimodal alignment and evaluation.
Source: Informatics
Authors: Shuxian Liu, Tianyi Li
Institution: Xinjiang University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
Award: 维吾尔文网络社会集群行为感知的关键技术研究
OpenAlex ID: W7124167379