Dynamic Scene Segmentation and Sentiment Analysis for Danmaku
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 techniqu...
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/8/4435 |
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| author | Limin Li Jie Jing Peng Shi |
| author_facet | Limin Li Jie Jing Peng Shi |
| author_sort | Limin Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-26509b2b9ffb467eb6ecd6a29e6c5073 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-26509b2b9ffb467eb6ecd6a29e6c50732025-08-20T02:24:42ZengMDPI AGApplied Sciences2076-34172025-04-01158443510.3390/app15084435Dynamic Scene Segmentation and Sentiment Analysis for DanmakuLimin Li0Jie Jing1Peng Shi2National Center for Materials Service Safety, University of Science and Technology Beijing, No. 12 Kunlun Road, Beijing 100083, ChinaNational Center for Materials Service Safety, University of Science and Technology Beijing, No. 12 Kunlun Road, Beijing 100083, ChinaNational Center for Materials Service Safety, University of Science and Technology Beijing, No. 12 Kunlun Road, Beijing 100083, ChinaDanmaku 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.https://www.mdpi.com/2076-3417/15/8/4435Danmakusentiment analysisscene segmentationapriori algorithmmacbert |
| spellingShingle | Limin Li Jie Jing Peng Shi Dynamic Scene Segmentation and Sentiment Analysis for Danmaku Applied Sciences Danmaku sentiment analysis scene segmentation apriori algorithm macbert |
| title | Dynamic Scene Segmentation and Sentiment Analysis for Danmaku |
| title_full | Dynamic Scene Segmentation and Sentiment Analysis for Danmaku |
| title_fullStr | Dynamic Scene Segmentation and Sentiment Analysis for Danmaku |
| title_full_unstemmed | Dynamic Scene Segmentation and Sentiment Analysis for Danmaku |
| title_short | Dynamic Scene Segmentation and Sentiment Analysis for Danmaku |
| title_sort | dynamic scene segmentation and sentiment analysis for danmaku |
| topic | Danmaku sentiment analysis scene segmentation apriori algorithm macbert |
| url | https://www.mdpi.com/2076-3417/15/8/4435 |
| work_keys_str_mv | AT liminli dynamicscenesegmentationandsentimentanalysisfordanmaku AT jiejing dynamicscenesegmentationandsentimentanalysisfordanmaku AT pengshi dynamicscenesegmentationandsentimentanalysisfordanmaku |