Title
Fine-grained emotion detection in suicide notes : a thresholding approach to multi-label classification Fine-grained emotion detection in suicide notes : a thresholding approach to multi-label classification
Author
Faculty/Department
Faculty of Arts. Linguistics and Literature
Publication type
article
Publication
Subject
Linguistics
Source (journal)
Biomedical Informatics Insights
Volume/pages
(2012) :5 , p. 61-69
ISSN
1178-2226
vabb
c:vabb:327539
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
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.
Full text (open access)
https://repository.uantwerpen.be/docman/irua/31d2b5/913120c1.pdf
Handle