Citation

Wicentowski R, Sydes MR. Biomed. Inform. Insights 2012; 5(Suppl 1): 51-60.

Affiliation

Swarthmore College, Computer Science Department, Swarthmore, PA, USA.

Copyright

(Copyright © 2012, Libertas Academica)

DOI

10.4137/BII.S8972

PMID

22879760

PMCID

PMC3409489

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.

Language: en