The AAAI-19 Workshop On Affective Content Analysis
AFFCON2019: Modeling Affect-In-Action
Venue: Kahili 1 (Level 6 of the Kalia Conference Center, The Hilton)
January 27 2019, Hilton Hawaiian Village, Honolulu, Hawaii, USA
Venue: Kahili 1 (Level 6 of the Kalia Conference Center, The Hilton)
January 27 2019, Hilton Hawaiian Village, Honolulu, Hawaii, USA
Affect analysis refers to the set of techniques which identify and measure the ‘experience of an emotion’. This interdisciplinary workshop focuses on analyzing affect in content including text, audio, images, and videos. The word ‘affective’ is used to refer to emotion, sentiment, personality, mood, and attitudes including subjective evaluations, opinions, and speculations. All methods and models that measure affective responses to content are in the scope of the workshop. We encourage interdisciplinary participation and cross-disciplinary approaches from computational linguistics (CL), consumer psychology, human-computer interaction (HCI), marketing science and cognitive science, among others.
The theme of the 2nd Affective Content Analysis workshop is "Modeling Affect-in-Action" and we're announcing a Shared Task (see CL-Aff Shared Task CFP) to encourage the development of new models and approaches for modeling happy moments. This Shared Task is based on the HappyDB corpus.
Models of affect have recently been adapted for user-generated content. However, the exponentially increasing size and the dynamic, multimedia nature of this data make it difficult to detect and measure affect. Furthermore, the subjective nature of human affect suggests the need to measure in ways that recognize multiple interpretations of human responses. A few key challenges are:
The AI community is well-poised to propose new solutions, approaches and frameworks to tackle these and other challenges. This workshop invites papers that address these and other topics, propose novel solutions for well-established problems, offer modeling and measurement of affect, and identify the best affect–related dimensions to study consumer behavior. Potential examples include deep learning for affect analysis, leveraging traditional affective computing algorithms (that are built on multi–modal data and sensors) for text and so on.
Another area of focus for this workshop is the need of standardized baselines, datasets, and evaluation metrics. Papers describing novel language resources, evaluation metrics and standards for affect analysis and understanding are also encouraged.
Published papers from AffCon@ AAAI 2018.
If you have any questions regarding the workshop scope or need further information, please do not hesitate to send an e-mail:
nchhaya [AT] adobe.com
jaidka [AT] sas.upenn.edu
Thank you!