Abstract
Affective lexicons have been commonly used as lexical features for depression classification, but their effectiveness is relatively unexplored in the literature. In this paper, we investigate the effectiveness of three popular affective lexicons in the task of depression classification. We also develop two lexical feature engineering strategies for incorporating those lexicons into a supervised classifier. The effectiveness of different lexicons and feature engineering strategies are evaluated on a depression dataset collected from LiveJournal.
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Acknowledgments
This work is supported by the award made by the UK Engineering and Physical Sciences Research Council (Grant number: EP/P005810/1).
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Yusof, N.F.A., Lin, C., Guerin, F. (2018). Assessing the Effectiveness of Affective Lexicons for Depression Classification. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science(), vol 10859. Springer, Cham. https://doi.org/10.1007/978-3-319-91947-8_7
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DOI: https://doi.org/10.1007/978-3-319-91947-8_7
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