Sentiment Analysis and Topic Recognition in Video Transcriptions

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Abstract
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.

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