The theme of AffCon 2020 is the study of affect in response to interactive content that may evolve over time. The word ‘affect’ is used to refer to emotion, sentiment, mood, and attitudes including subjective evaluations, opinions, and speculations. Psychological models of affect have been adopted by other disciplines to conceptualize and measure users’ opinions, intentions, and expressions. However, the context-specific characteristics of human affect suggest the need to measure in ways that recognize multiple interpretations of human responses.
We invite papers that offer modeling and measurement of affect and identify the important affect–related dimensions to study consumer behavior. In turn, that allows data models to be more informed in representing behaviors and hence effective in guiding decisions and actions by firms. We welcome submissions on topics including - but not limited to - the following:
We especially invite papers investigating multiple related themes, industry papers, and descriptions of running projects and ongoing work. To address the scarcity of standardized baselines, datasets, and evaluation metrics for cross-disciplinary affective content analysis, submissions describing new language resources, evaluation metrics, and standards for affect analysis and understanding are also strongly encouraged.
Pre-published/in-press work is also invited as a part of a short presentation and poster session to encourage discussions and introductions across communities. These submissions will not be re-published but a few selected papers will be accepted for posters and short presentations.
Submissions should be made via EasyChair and must follow the formatting guidelines for AAAI-2020 (use the AAAI Author Kit). All submissions must be anonymous and conform to AAAI standards for double-blind review. Both full papers (8 pages including references) and short papers (4 pages including references) that adhere to the 2-column AAAI format will be considered for review.
There is a growing interest in understanding how humans initiate and hold conversations. The affective understanding of conversations focuses on the problem of how speakers use affect to react to a situation and to each other. We introduce the OffMyChest Conversation dataset, and invite submissions for the Computational Linguistics Affect Understanding (CL-Aff) Shared Task on Affect in Conversations.
See CL-Aff for Dataset details, Task details and deadlines.