JCSE, vol. 6, no. 2, pp.143-150, June, 2012
DOI: http://dx.doi.org/10.5626/JCSE.2012.6.2.143
Topic Classification for Suicidology
Jonathon Read, Erik Velldal, Lilja Øvrelid Language Technology Group, Department of Informatics, University of Oslo, Norway
Abstract: Computational techniques for topic classification can support qualitative research by automatically applying labels in
preparation for qualitative analyses. This paper presents an evaluation of supervised learning techniques applied to one
such use case, namely, that of labeling emotions, instructions and information in suicide notes. We train a collection of
one-versus-all binary support vector machine classifiers, using cost-sensitive learning to deal with class imbalance. The
features investigated range from a simple bag-of-words and n-grams over stems, to information drawn from syntactic
dependency analysis and WordNet synonym sets. The experimental results are complemented by an analysis of systematic
errors in both the output of our system and the gold-standard annotations.
Keyword:
Affect recognition; Sentiment analysis; Skewed class distribution; Text classification
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