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        <title>Combining Heterogeneous User Generated Data to Sense Well-being</title>
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        <namePart type="given">Adam</namePart>
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        <namePart type="given">Maria</namePart>
        <namePart type="family">Liakata</namePart>
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    <name type="personal">
        <namePart type="given">Brigitte</namePart>
        <namePart type="family">Jellinek</namePart>
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    <name type="personal">
        <namePart type="given">Weisi</namePart>
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    <name type="personal">
        <namePart type="given">Alexandra</namePart>
        <namePart type="family">Cristea</namePart>
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            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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        <name type="personal">
            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <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 correlation coefficient of 0.68-0.87 which is higher than the state-of-the art in equivalent multi-modal tasks for affect.</abstract>
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%0 Conference Proceedings
%T Combining Heterogeneous User Generated Data to Sense Well-being
%A Tsakalidis, Adam
%A Liakata, Maria
%A Damoulas, Theo
%A Jellinek, Brigitte
%A Guo, Weisi
%A Cristea, Alexandra
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F tsakalidis-etal-2016-combining
%X 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 correlation coefficient of 0.68-0.87 which is higher than the state-of-the art in equivalent multi-modal tasks for affect.
%U https://aclanthology.org/C16-1283
%P 3007-3018

Markdown (Informal)

[Combining Heterogeneous User Generated Data to Sense Well-being](https://aclanthology.org/C16-1283) (Tsakalidis et al., COLING 2016)

• Combining Heterogeneous User Generated Data to Sense Well-being (Tsakalidis et al., COLING 2016)

ACL

• Adam Tsakalidis, Maria Liakata, Theo Damoulas, Brigitte Jellinek, Weisi Guo, and Alexandra Cristea. 2016. Combining Heterogeneous User Generated Data to Sense Well-being. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3007–3018, Osaka, Japan. The COLING 2016 Organizing Committee.