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Combining heterogeneous user generated data to sense well-being.

Tsakalidis, Adam and Liakata, Maria and Damoulas, Theodoros and Jellinek, Brigitte and Guo, Weisi and Cristea, A. I. (2016) 'Combining heterogeneous user generated data to sense well-being.', in Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics : Technical Papers. , pp. 3007-3018.


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.

Item Type:Book chapter
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Publisher statement:This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details:
Date accepted:21 September 2016
Date deposited:31 July 2018
Date of first online publication:01 December 2016
Date first made open access:No date available

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