Fine-Grained Emotion Detection in Suicide Notes: A Thresholding Approach to Multi-Label Classification
Title | Fine-Grained Emotion Detection in Suicide Notes: A Thresholding Approach to Multi-Label Classification |
Publication Type | Journal Article |
Year of Publication | 2012 |
Authors | Luyckx, K, Vaassen, F, Peersman, C, Daelemans, W |
Journal | Biomedical Information Insights (BII) |
Volume | 5 |
Pagination | 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, 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. |
Refereed Designation | Refereed |