Fine-Grained Emotion Detection in Suicide Notes:
A Thresholding Approach to Multi-Label Classification

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.

http://www.clips.ua.ac.be/sites/default/files/f_bii-fine-grained-emotion-detection-in-suicide-notes-a-thresholding-approach_4099.pdf