HOTVCOM: Generating Buzzworthy Comments for Videos
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917783084728320 |
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| author | Chen, Yuyan Qian, Yiwen Yan, Songzhou Jia, Jiyuan Li, Zhixu Xiao, Yanghua Li, Xiaobo Yang, Ming Guo, Qingpei |
| author_facet | Chen, Yuyan Qian, Yiwen Yan, Songzhou Jia, Jiyuan Li, Zhixu Xiao, Yanghua Li, Xiaobo Yang, Ming Guo, Qingpei |
| contents | In the era of social media video platforms, popular ``hot-comments'' play a crucial role in attracting user impressions of short-form videos, making them vital for marketing and branding purpose. However, existing research predominantly focuses on generating descriptive comments or ``danmaku'' in English, offering immediate reactions to specific video moments. Addressing this gap, our study introduces \textsc{HotVCom}, the largest Chinese video hot-comment dataset, comprising 94k diverse videos and 137 million comments. We also present the \texttt{ComHeat} framework, which synergistically integrates visual, auditory, and textual data to generate influential hot-comments on the Chinese video dataset. Empirical evaluations highlight the effectiveness of our framework, demonstrating its excellence on both the newly constructed and existing datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15196 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HOTVCOM: Generating Buzzworthy Comments for Videos Chen, Yuyan Qian, Yiwen Yan, Songzhou Jia, Jiyuan Li, Zhixu Xiao, Yanghua Li, Xiaobo Yang, Ming Guo, Qingpei Computer Vision and Pattern Recognition Artificial Intelligence In the era of social media video platforms, popular ``hot-comments'' play a crucial role in attracting user impressions of short-form videos, making them vital for marketing and branding purpose. However, existing research predominantly focuses on generating descriptive comments or ``danmaku'' in English, offering immediate reactions to specific video moments. Addressing this gap, our study introduces \textsc{HotVCom}, the largest Chinese video hot-comment dataset, comprising 94k diverse videos and 137 million comments. We also present the \texttt{ComHeat} framework, which synergistically integrates visual, auditory, and textual data to generate influential hot-comments on the Chinese video dataset. Empirical evaluations highlight the effectiveness of our framework, demonstrating its excellence on both the newly constructed and existing datasets. |
| title | HOTVCOM: Generating Buzzworthy Comments for Videos |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2409.15196 |