Recently, multimodal sentiment analysis has seen remarkable advance and a lot of datasets are proposed for its development. In general, current multimodal sentiment analysis datasets usually follow the traditional system of sentiment/emotion, such as positive, negative and so on. However, when applied in the scenario of video recommendation, the traditional sentiment/emotion system is hard to be leveraged to represent different contents of videos in the perspective of visual senses and language understanding. Based on this, we propose a multimodal sentiment analysis dataset, named baiDu Video Sentiment dataset (DuVideoSenti), and introduce a new sentiment system which is designed to describe the sentimental style of a video on recommendation scenery. Specifically, DuVideoSenti consists of 5,630 videos which displayed on Baidu, each video is manually annotated with a sentimental style label which describes the user's real feeling of a video. Furthermore, we propose UNIMO as our baseline for DuVideoSenti. Experimental results show that DuVideoSenti brings new challenges to multimodal sentiment analysis, and could be used as a new benchmark for evaluating approaches designed for video understanding and multimodal fusion. We also expect our proposed DuVideoSenti could further improve the development of multimodal sentiment analysis and its application to video recommendations.

中文翻译：

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用于视频推荐的多模态情感数据集

最近，多模态情感分析取得了显着进展，并提出了许多数据集用于其发展。总的来说，当前的多模态情感分析数据集通常遵循传统的情感/情感系统，如正面、负面等。然而，当应用于视频推荐场景时，传统的情感/情感系统很难从视觉和语言理解的角度来表示视频的不同内容。基于此，我们提出了一个多模态情感分析数据集，命名为百度视频情感数据集（DuVideoSenti），并引入了一种新的情感系统，旨在描述推荐场景上视频的情感风格。具体来说，DuVideoSenti 由 5,630 个在百度上显示的视频组成，每个视频都手动标注了情感风格标签，描述了用户对视频的真实感受。此外，我们建议 UNIMO 作为 DuVideoSenti 的基线。实验结果表明，DuVideoSenti 给多模态情感分析带来了新的挑战，可以作为评估视频理解和多模态融合方法的新基准。我们还期望我们提出的 DuVideoSenti 可以进一步改进多模态情感分析的发展及其在视频推荐中的应用。并且可以用作评估为视频理解和多模态融合设计的方法的新基准。我们还期望我们提出的 DuVideoSenti 可以进一步改进多模态情感分析的发展及其在视频推荐中的应用。并且可以用作评估为视频理解和多模态融合设计的方法的新基准。我们还期望我们提出的 DuVideoSenti 可以进一步改进多模态情感分析的发展及其在视频推荐中的应用。