Publication details for Professor Alexandra CristeaTsakalidis, Adam Liakata, Maria Damoulas, Theodoros Jellinek, Brigitte Guo, Weisi & Cristea, A. I. (2016), Combining heterogeneous user generated data to sense well-being, in Matsumoto, Yuji & Prasad, Rashmi eds, COLING 2016. Osaka, The COLING 2016 Organizing Committee, 3007-3018.
- Publication type: Conference Paper
- ISSN/ISBN: 9784879747020
- Further publication details on publisher web site
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Author(s) from Durham
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.