Document Type
Article
Publication Date
1-30-2012
Published In
Biomedical Informatics Insights
Abstract
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 F 1 score of 0.534.
Recommended Citation
Richard H. Wicentowski and M. R. Sydes.
(2012).
"Emotion Detection In Suicide Notes Using Maximum Entropy Classification".
Biomedical Informatics Insights.
Volume 5,
Issue Suppl. 1.
51-60.
DOI: 10.4137/BII.S8972
https://works.swarthmore.edu/fac-comp-sci/1
Comments
This work is freely available courtesy of Libertas Academica.