Combining heterogeneous user generated data to sense well-being

Tsakalidis, Adam; Liakata, Maria; Damoulas, Theodoros; Jellinek, Brigitte; Guo, Weisi; Cristea, A.I.

Abstract

In this paper we address a new problem of predicting affect and well-being scales in a real-world setting of heterogeneous, longitudinal and non-synchronous textual as well as non-linguistic data that can be harvested from on-line media and mobile phones. We describe the method for collecting the heterogeneous longitudinal data, how features are extracted to address missing information and differences in temporal alignment, and how the latter are combined to yield promising predictions of affect and well-being on the basis of widely used psychological scales. We achieve a coefficient of determination (R2 ) of 0.71 − 0.76 and a ρ of 0.68 − 0.87 which is higher than the state-of-the art in equivalent multi-modal tasks for affect.

Citation

Tsakalidis, A., Liakata, M., Damoulas, T., Jellinek, B., Guo, W., & Cristea, A. (2016, December). Combining heterogeneous user generated data to sense well-being. Presented at COLING 2016, Osaka

• Presentation Conference Type: Conference Paper (published)
• Conference Name: COLING 2016
• Acceptance Date: Sep 21, 2016
• Online Publication Date: Dec 1, 2016
• Publication Date: Dec 1, 2016
• Deposit Date: Jul 11, 2018
• Publicly Available Date: Jul 31, 2018
• Pages: 3007-3018
• Book Title: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics : Technical Papers.
• Public URL: https://durham-repository.worktribe.com/output/1144363
• Publisher URL: http://coling2016.anlp.jp/
• Related Public URLs: http://wrap.warwick.ac.uk/81828/