Modern Social Science Research

In the current era of increasingly diversified and intelligent information dissemination, emotion plays an increasingly important role in the expression and reception of news content. Traditional sentiment recognition technologies rely heavily on text features, making it difficult to fully capture the emotional information contained in non-linguistic modalities such as voice, image, and video, resulting in limited accuracy and depth of sentiment understanding. With the rapid development of deep learning technology, multimodal sentiment recognition (MSR) has become a key path to break through this bottleneck. By integrating multimodal data such as language, vision, and hearing, this technology can more comprehensively and accurately interpret the emotional connotations in news dissemination, thereby enhancing the level of intelligence in content production and the personalized quality of user experience. This paper aims to explore the practical paths and realistic challenges of deep learning-based multimodal sentiment recognition technology in news dissemination, attempting to build an effective connection between theory and practice, with the hope of providing a feasible reference for sentiment perception and dissemination optimization in the era of smart media.

Deep learning; Multimodal; Sentiment analysis technology; News dissemination; Practical exploration