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A Proposal for Book Oriented Aspect Based Sentiment Analysis: Comparison over Domains Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Assessing the Effectiveness of Affective Lexicons for Depression Classification

Authors : Noor Fazilla Abd Yusof, Chenghua Lin, Frank Guerin

Published in: Natural Language Processing and Information Systems

Publisher: Springer International Publishing

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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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Footnotes
Literature
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Smith, K., Renshaw, P., Bilello, J.: The diagnosis of depression: current and emerging methods. J. Compr. Psychiatry 54, 1–6 (2013)CrossRef
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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)
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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)
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Bradley, M., Lang, P.: Affective Norms for English Words (ANEW): instruction manual and affective ratings. Technical report C-2, University of Florida (2010)
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Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: EMNLP-HLT, pp. 347–354 (2005)
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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)
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Metadata
Title
Assessing the Effectiveness of Affective Lexicons for Depression Classification
Authors
Noor Fazilla Abd Yusof
Chenghua Lin
Frank Guerin
Copyright Year
2018
Book
Natural Language Processing and Information Systems

Print ISBN: 978-3-319-91946-1

Electronic ISBN: 978-3-319-91947-8

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-91947-8_7

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