@InProceedings{tsakalidis-EtAl:2016:COLING,
  author    = {Tsakalidis, Adam  and  Liakata, Maria  and  Damoulas, Theo  and  Jellinek, Brigitte  and  Guo, Weisi  and  Cristea, Alexandra},
  title     = {Combining Heterogeneous User Generated Data to Sense Well-being},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {3007--3018},
  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
	(R\^{}2) of 0.71-0.76 and a correlation coefficient of 0.68-0.87 which is higher
	than the state-of-the art in equivalent multi-modal tasks for affect.},
  url       = {http://aclweb.org/anthology/C16-1283}
}