Affective Generative Visuals Based on Data Input Influenced by User’s Emotions
- DOI
- 10.2991/978-2-38476-136-4_33How to use a DOI?
- Keywords
- Generative Visualisation; Affective Data; Computer-mediated Environment
- Abstract
Generative art, a subdomain of new media art focuses on an autonomous system created by the artist. Personal data in the digital environment are becoming increasingly valuable, especially within the globalising environment which is the key development of organisations. However, the data visualisation of personal data is being consumed by global industries and not merely the data provider itself. The shift in consumption needs to be further explored to allow data providers to have emotional valuation and visualisation of their personal well-being in a digital environment. This research addresses the topic by focusing on the potential of generative visualisation based on affective data in a creative collaboration environment. Generative visualisation is necessary to study the emotion valuation process. The findings of the study identify generative visuals as an affective data visualisation method. The result is to expand the function of generative visualisation and the significance of emotions in creative collaboration based on a computer-mediated environment.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Kin Keong Lee PY - 2023 DA - 2023/11/06 TI - Affective Generative Visuals Based on Data Input Influenced by User’s Emotions BT - Proceedings of the International Conference of Innovation in Media and Visual Design (IMDES 2023) PB - Atlantis Press SP - 387 EP - 402 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-136-4_33 DO - 10.2991/978-2-38476-136-4_33 ID - Lee2023 ER -