In The Mood For Vlog: Multimodal Inference In Conversational Social Video

The prevalent "share what's on your mind" paradigm of social media can be examined from the perspective of mood: short-term affective states revealed by the shared data. This view takes on new relevance given the emergence of conversational social video as a popular genre among viewers looking for entertainment and among video contributors as a channel for debate, expertise sharing, and artistic expression. From the perspective of human behavior understanding, in conversational social video both verbal and nonverbal information is conveyed by speakers and decoded by viewers. We present a systematic study of classification and ranking of mood impressions in social video, using vlogs from YouTube. Our approach considers eleven natural mood categories labeled through crowdsourcing by external observers on a diverse set of conversational vlogs. We extract a comprehensive number of nonverbal and verbal behavioral cues from the audio and video channels to characterize the mood of vloggers. Then we implement and validate vlog classification and vlog ranking tasks using supervised learning methods. Following a reliability and correlation analysis of the mood impression data, our study demonstrates that, while the problem is challenging, several mood categories can be inferred with promising performance. Furthermore, multimodal features perform consistently better than single-channel features. Finally, we show that addressing mood as a ranking problem is a promising practical direction for several of the mood categories studied.