@article{ref1,
title="Fine-grained emotion detection in suicide notes: a thresholding approach to multi-label classification",
journal="Biomedical informatics insights",
year="2012",
author="Luyckx, Kim and Vaassen, Frederik and Peersman, Claudia and Daelemans, Walter",
volume="5",
number="Suppl 1",
pages="61-69",
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.<p /> <p>Language: en</p>",
language="en",
issn="1178-2226",
doi="10.4137/BII.S8966",
url="http://dx.doi.org/10.4137/BII.S8966"
}