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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Smith, K., Renshaw, P., Bilello, J.: The diagnosis of depression: current and emerging methods. J. Compr. Psychiatry 54, 1–6 (2013)
Inkster, B., Stillwell, D., Kosinski, M., Jones, P.: A decade into Facebook where is psychiatry in the digital age. J. Lancet Psychiatry 01 (2016)
De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Characterizing and predicting postpartum depression from shared Facebook data. In: CSCW, pp. 626–638 (2014)
Resnik, P., Armstrong, W., Claudino, L., Nguyen, T., Nguyen, V., Boyd-graber, J.: Beyond LDA: exploring supervised topic modeling for depression-related language in Twitter. In: CLPsych, pp. 99–107 (2015)
Yusof, N.F.A., Lin, C., Guerin, F.: Analysing the causes of depressed mood from depression vulnerable individuals. In: DDDSM Workshop at IJCNLP, pp. 9–17 (2017)
Bradley, M., Lang, P.: Affective Norms for English Words (ANEW): instruction manual and affective ratings. Technical report C-2, University of Florida (2010)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: EMNLP-HLT, pp. 347–354 (2005)
Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC 10, pp. 2200–2204 (2010)
Kibriya, A.M., Frank, E., Pfahringer, B., Holmes, G.: Multinomial naive bayes for text categorization revisited. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 488–499. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30549-1_43
Acknowledgments
This work is supported by the award made by the UK Engineering and Physical Sciences Research Council (Grant number: EP/P005810/1).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-91947-8_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91946-1
Online ISBN: 978-3-319-91947-8
eBook Packages: Computer ScienceComputer Science (R0)