The AAAI-20 Workshop On Affective Content Analysis
AFFCON2020: Interactive Affective Response
February 7, 2020
Room Details: Bryant, 2nd Floor
Hilton New York Midtown
New York, USA
February 7, 2020
Room Details: Bryant, 2nd Floor
Hilton New York Midtown
New York, 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 this year, `Affect in Response', the study of affect in response to interactive content that may evolve over time. Most analysis in affective content has focused on the study of static data such as reviews or other content that was generated in the past. Our goal is to focus on affective content in interactions including: dialogues, chatbots, avatars interactions, and multi-modal interfaces. We invite research that explores how to make affective content in evolving interactions more dynamic and responsive.
Once again we're announcing a Shared Task to encourage the development of new models and approaches for affect in interactions.
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 2019.
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!