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@ARTICLE{Sanchez-Cortes_ACMTIIS_2015,
         author = {Sanchez-Cortes, Dairazalia and Kumano, Shiro and Otsuka, Kazuhiro and Gatica-Perez, Daniel},
       projects = {Idiap, NISHA, UBIMPRESSED},
          month = jul,
          title = {In the Mood for Vlog: Multimodal Inference in Conversational Social Video},
        journal = {ACM Transactions on Interactive Intelligent Systems},
         volume = {5},
         number = {2},
           year = {2015},
            doi = {10.1145/2641577},
       abstract = {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 in-
formation 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 conversa-
tional 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 consis-
tently 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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2015/Sanchez-Cortes_ACMTIIS_2015.pdf}
}