- 期刊
- OpenAccess
Emotion Detection in Suicide Notes using Maximum Entropy Classification
並列摘要
An ensemble of supervised maximum entropy classifiers can accurately detect and identify sentiments expressed in suicide notes. Using lexical and syntactic features extracted from a training set of externally annotated suicide notes, we trained separate classifiers for each of fifteen pre-specified emotions. This formed part of the 2011 i2b2 NLP Shared Task, Track 2. The precision and recall of these classifiers related strongly with the number of occurrences of each emotion in the training data. Evaluating on previously unseen test data, our best system achieved an F1 score of 0.534.
延伸閱讀
- Luyckx, K., Vaassen, F., Peersman, C., & Daelemans, W. (2012). Fine-Grained Emotion Detection in Suicide Notes: A Thresholding Approach to Multi-Label Classification. Biomedical Informatics Insights, (), 61-69. https://doi.org/10.4137/BII.S8966
- Cherry, C., Mohammad, S. M., & Bruijn, B. D. (2012). Binary Classifiers and Latent Sequence Models for Emotion Detection in Suicide Notes. Biomedical Informatics Insights, (), 147-154. https://doi.org/10.4137/BII.S8933
- Pak, A., Bernhard, D., Paroubek, P., & Grouin, C. (2012). A Combined Approach to Emotion Detection in Suicide Notes. Biomedical Informatics Insights, (), 105-114. https://doi.org/10.4137/BII.S8969
- Hsieh, K. R., & Chang, W. H. (2010). An Effective Classification for Emotion Detection. In 遠東科技大學電機工程系 (Ed.), 智慧型系統工程應用研討會 (pp. 272-276). 遠東科技大學電機工程系. https://doi.org/10.30176/ISC.201005.0272
- Desmet, B., & Hoste, V. (2012). Combining Lexico-semantic Features for Emotion Classification in Suicide Notes. Biomedical Informatics Insights, (), 125-128. https://doi.org/10.4137/BII.S8960