Exploring the practice of multimodal sentiment recognition technology based on deep learning in news communication
基于深度学习的多模态情感识别技术在新闻传播中的实践探索

摘要

在信息传播日趋多样化和智能化的当下，情感在新闻内容的表达与接受中扮演着愈发重要的角色。传统的情感识别技术多依赖于文本特征，难以充分捕捉语音、图像、视频等非语言模态中蕴含的情绪信息，导致情感理解的准确率与深度仍有限。随着深度学习技术的迅速发展，多模态情感识别（Multimodal Sentiment Recognition, MSR）成为突破这一瓶颈的关键路径。该技术通过融合语言、视觉与听觉等多模态数据，能够更加全面、精准地解读新闻传播中的情绪内涵，进而提升内容生产的智能化水平与用户体验的个性化质量。本文旨在探讨基于深度学习的多模态情感识别技术在新闻传播中的实践路径与现实挑战，尝试构建理论与实务之间的有效衔接，以期为智慧媒体时代的情绪感知与传播优化提供可行参考。

Abstract

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.