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Title
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Fine-grained emotion detection in suicide notes : a thresholding approach to multi-label classification
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Author
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Abstract
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| We present a system to automatically identify emotion-carrying sentences in suicide notes and to detect the specific fine-grained emotion conveyed. With this system, we competed in Track 2 of the 2011 Medical NLP Challenge,14 where the task was to distinguish between fifteen emotion labels, from guilt, sorrow, and hopelessness to hopefulness and happiness. Since a sentence can be annotated with multiple emotions, we designed a thresholding approach that enables assigning multiple labels to a single instance. We rely on the probability estimates returned by an SVM classifier and experimentally set thresholds on these probabilities. Emotion labels are assigned only if their probability exceeds a certain threshold and if the probability of the sentence being emotion-free is low enough. We show the advantages of this thresholding approach by comparing it to a naïve system that assigns only the most probable label to each test sentence, and to a system trained on emotion-carrying sentences only. |
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Language
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English
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Source (journal)
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Biomedical Informatics Insights
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Publication
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2012
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ISSN
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1178-2226
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DOI
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10.4137/BII.S8966
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Volume/pages
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5
(2012)
, p. 61-69
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Full text (Publisher's DOI)
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Full text (open access)
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