Although video affective content analysis has great potential in many applications, it has not been thoroughly studied due to limited datasets. In this paper, we construct a large-scale video affective dataset with danmu (VAD). It consists of 19,267 elaborately segmented video clips from user-generated videos. The VAD dataset is annotated by the crowdsourcing platform with discrete valence, arousal, and primary emotions, as well as the comparison of valence and arousal between two consecutive video clips. Unlike previous datasets, including only video clips, our proposed dataset also provides danmu, which is the real-time comment from users as they watch a video. Danmu provides extra information for video affective content analysis. As a preliminary assessment of the usability of our dataset, an analysis of inter-annotator consistency for each label is conducted using weighted Fleiss' Kappa, regular Fleiss' Kappa, intraclass correlation coefficient, and percent consensus. Besides, we also perform a statistical analysis of labels and danmu. Finally, video affective content analysis is conducted on our dataset and three typical methods (i.e., TFN, MulT, and MISA) are leveraged to provide benchmarks. We also demonstrate that danmu can significantly improve the performance of the video affective content analysis task on some labels. Our dataset is available for research purposes.

中文翻译：

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VAD：带有 Danmu 的视频情感数据集


尽管视频情感内容分析在许多应用中具有巨大潜力，但由于数据集有限，尚未得到深入研究。在本文中，我们使用弹幕（VAD）构建了大规模视频情感数据集。它由 19,267 个来自用户生成视频的精心分段的视频剪辑组成。 VAD数据集由众包平台标注离散效价、唤醒度和主要情绪，以及两个连续视频剪辑之间效价和唤醒度的比较。与以前的数据集（仅包括视频剪辑）不同，我们提出的数据集还提供弹幕，这是用户观看视频时的实时评论。弹幕为视频情感内容分析提供了额外的信息。作为对我们数据集可用性的初步评估，使用加权 Fleiss' Kappa、常规 Fleiss' Kappa、类内相关系数和一致性百分比对每个标签的注释者间一致性进行分析。此外，我们还对标签和弹幕进行了统计分析。最后，对我们的数据集进行视频情感内容分析，并利用三种典型方法（即 TFN、Mult 和 MISA）来提供基准。我们还证明了弹幕可以显着提高某些标签上的视频情感内容分析任务的性能。我们的数据集可用于研究目的。