- 期刊
- OpenAccess
Fine-Grained Emotion Detection in Suicide Notes: A Thresholding Approach to Multi-Label Classification
並列摘要
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.
延伸閱讀
- Wicentowski, R., & Sydes, M. R. (2012). Emotion Detection in Suicide Notes using Maximum Entropy Classification. Biomedical Informatics Insights, (), 51-60. https://doi.org/10.4137/BII.S8972
- Sohn, S., Torii, M., Li, D., Wagholikar, K., Wu, S., & Liu, H. (2012). A Hybrid Approach to Sentiment Sentence Classification in Suicide Notes. Biomedical Informatics Insights, (), 43-50. https://doi.org/10.4137/BII.S8961
- Yang, H., Willis, A., Roeck, A. D., & Nuseibeh, B. (2012). A Hybrid Model for Automatic Emotion Recognition in Suicide Notes. Biomedical Informatics Insights, (), 17-30. https://doi.org/10.4137/BII.S8948
- Liakata, M., Kim, J. Y., Saha, S., astings, J., & Schuhmann, D. R. (2012). Three Hybrid Classifiers for the Detection of Emotions in Suicide Notes. Biomedical Informatics Insights, (), 175-184. https://doi.org/10.4137/BII.S8967
- Yeh, E., Jarrold, W., & Jordan, J. (2012). Leveraging Psycholinguistic Resources and Emotional Sequence Models for Suicide Note Emotion Annotation. Biomedical Informatics Insights, (), 155-163. https://doi.org/10.4137/BII.S8979