计算机工程与应用2025，Vol.61Issue(23)：110-125,16.DOI:10.3778/j.issn.1002-8331.2510-0100
基于多模态情感数据的网络视频满意度分析方法
Method for Analyzing Satisfaction with Online Videos Based on Multimodal Emotional Data
摘要
Abstract
With the rapid development of the internet and video platforms,online video content has become increasingly diverse.Effectively evaluating user satisfaction with different types of online videos has emerged as a critical issue in video content promotion and human-computer interaction research.Although multimodal sentiment analysis methods integrating text,audio,and vision information have been widely applied to user emotion recognition,emotional states alone cannot fully reflect users'comprehensive experience of content.Existing research often remains confined to modeling affect polarity,neglecting the underlying mechanisms linking emotion to satisfaction.This has led to the long-term oversight of satisfaction as a higher-order psychological construct.To more accurately assess users'holistic emotional responses to online videos,the paper proposes MVSA(multimodal video satisfaction analysis),a video satisfaction analysis framework based on multimodal fusion.Concurrently,the paper establishes MVS-Eval(multimodal video satisfaction evaluation),the first multimodal dataset specifically designed for online video user satisfaction research.This dataset encompasses satisfaction tags across multiple dimensions,including attractiveness,concentration,and engagement.This aims to com-prehensively model users'subjective feedback on video content.Furthermore,the paper proposes the multimodal satisfac-tion estimation algorithm MUSE(multimodal understanding for satisfaction estimation),based on modality consistency training and satisfaction-guided fusion mechanisms.This effectively establishes the emotion-satisfaction link and enhances the model's satisfaction metric prediction performance and cross-scenario generalization capability.Additionally,the MVSA framework integrates an intelligent feedback processing platform that automatically parses user feedback videos and generates structured satisfaction evaluation results.Experimental results demonstrate that MUSE significantly outper-forms existing mainstream models across multiple benchmark tasks,validating its effectiveness and interpretability in modeling satisfaction for diverse online video types.关键词
网络视频/多模态数据/满意度分析Key words
online video/multimodal data/satisfaction analysis分类
信息技术与安全科学引用本文复制引用
王安启,李明轩,程泊宣..基于多模态情感数据的网络视频满意度分析方法[J].计算机工程与应用,2025,61(23):110-125,16.基金项目
国家重点研发计划项目(2023YFB3106302). （2023YFB3106302）
