Machine Learning
Vocal communication of emotion: a review of research paradigms
Speech Communication - Special issue on speech and emotion
Understanding how bloggers feel: recognizing affect in blog posts
CHI '06 Extended Abstracts on Human Factors in Computing Systems
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Emotion Recognition Based on Joint Visual and Audio Cues
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Faces of pain: automated measurement of spontaneousallfacial expressions of genuine and posed pain
Proceedings of the 9th international conference on Multimodal interfaces
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Using linguistic cues for the automatic recognition of personality in conversation and text
Journal of Artificial Intelligence Research
VlogSense: Conversational behavior and social attention in YouTube
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
Towards multimodal sentiment analysis: harvesting opinions from the web
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
Classification and pattern discovery of mood in weblogs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Understanding communicative emotions from collective external observations
CHI '12 Extended Abstracts on Human Factors in Computing Systems
FaceTube: predicting personality from facial expressions of emotion in online conversational video
Proceedings of the 14th ACM international conference on Multimodal interaction
Techniques and applications for sentiment analysis
Communications of the ACM
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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.