Inferring Mood in Ubiquitous Conversational Video

Conversational social video is becoming a worldwide trend. Video communication allows a more natural interaction, when aiming to share personal news, ideas, and opinions, by transmitting both verbal content and nonverbal behavior. However, the automatic analysis of natural mood is challenging, since it is displayed in parallel via voice, face, and body. This paper presents an automatic approach to infer 11 natural mood categories in conversational social video using single and multimodal nonverbal cues extracted from video blogs (vlogs) from YouTube. The mood labels used in our work were collected via crowdsourcing. Our approach is promising for several of the studied mood categories. Our study demonstrates that although multimodal features perform better than single channel features, not always all the available channels are needed to accurately discriminate mood in videos.


Presented at:
12th International Conference on Mobile and Ubiquitous Multimedia, Luleå, Sweden
Year:
2013
Publisher:
ACM Press
Keywords:
Laboratories:




 Record created 2013-12-19, last modified 2018-03-17

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