Nowadays, videos are an integral modality for information sharing on the World Wide Web. However, systems able to automatically understand the content and sentiment of a video are still in their infancy. Linguistic information transported in spoken parts of a video is known to convey valuable properties in regards to context and emotions. In this article, we explore a lexical knowledge-based extraction approach to obtain such understanding from the video transcriptions of a large-scale multimodal dataset (MuSe-CAR). To this end, we use SenticNet to extract natural language concepts and fine-tune several feature types on a subset of MuSe-CAR. With these features, we explore the content of a video as well as learning to predict emotional valence, arousal, and speaker topic classes. Our best model improves the linguistic baseline from the MuSe-Topic 2020 subchallenge by almost 3% (absolute) for the prediction of valence on the predefined challenge metric and outperforms a variety of baseline systems that require much higher computational power than the one proposed herein.

中文翻译：

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视频转录中的情感分析和主题识别


如今，视频已成为万维网上信息共享的不可或缺的方式。然而，能够自动理解视频内容和情感的系统仍处于起步阶段。众所周知，视频语音部分中传输的语言信息可以传达有关上下文和情感的有价值的属性。在本文中，我们探索了一种基于词汇知识的提取方法，以从大规模多模态数据集（MuSe-CAR）的视频转录中获得这种理解。为此，我们使用 SenticNet 提取自然语言概念，并在 MuSe-CAR 的子集上微调几种特征类型。借助这些功能，我们可以探索视频的内容，并学习预测情绪效价、唤醒度和演讲者主题类别。我们的最佳模型将 MuSe-Topic 2020 子挑战的语言基线提高了近 3%（绝对值），用于预测预定义挑战指标的效价，并且优于各种需要比本文提出的计算能力高得多的计算能力的基线系统。