Paper Info
Title
Inferring mood in ubiquitous conversational video
Abstract
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.
Year
DOI
Venue
2013
10.1145/2541831.2541864
MUM
Keywords
Field
DocType
natural interaction,discriminate mood,natural mood,mood category,ubiquitous conversational video,conversational social video,inferring mood,video blogs,automatic analysis,natural mood category,automatic approach,video communication,sentiment analysis
Mood,Natural interaction,Computer science,Sentiment analysis,Crowdsourcing,Nonverbal communication,Human–computer interaction,Multimedia,Nonverbal behavior
Conference
Citations 
PageRank 
References 
9
0.50
26
Authors
6
Name
Order
Citations
PageRank
Dairazalia Sanchez-Cortes11396.54
Joan-Isaac Biel225615.62
Shiro Kumano314916.82
Junji Yamato41120165.72
Kazuhiro Otsuka561954.15
Daniel Gatica-Perez64182276.74