Dynamic Scene Segmentation and Sentiment Analysis for Danmaku
Limin Li, Jie Jing, Peng Shi · Applied Sciences · 2025
Danmaku analysis is important for understanding video content and user interactions. However, current methods often look at separate comments and do not see the complex links between Danmaku and the video’s context. This paper presents a new approach that combines advanced shot segmentation techniques, using Deep Convolutional Neural Networks (DDCNN), with an analysis of feelings based on the MacBERT model. First, videos are cut into clear scenes based on detected scene changes. Then, a large group of Danmaku comments are collected and studied to make a complete dictionary of feelings for this field. With this as a base, a new Danmaku-E model is made to find and group seven different emotional categories within Danmaku comments. The model shows significantly improved performance, with accuracy increasing from 94.58% to 95.37% and F1 score going from 94.92% to 95.66%, helped by the improved dictionary of feelings. Experimental results show the good effects of the expanded dictionary in helping model performance in different structures. Also, the Apriori algorithm is used to find and explain links between Danmaku comments and video content, providing a deeper understanding into user participation and emotional reactions.