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. 2012;5(Suppl. 1):43-50.
doi: 10.4137/BII.S8961. Epub 2012 Jan 30.

A hybrid approach to sentiment sentence classification in suicide notes

Sunghwan Sohn  1 , Manabu Torii, Dingcheng Li, Kavishwar Wagholikar, Stephen Wu, Hongfang Liu
Affiliations

A hybrid approach to sentiment sentence classification in suicide notes

Sunghwan Sohn et al. Biomed Inform Insights. 2012.

Abstract

This paper describes the sentiment classification system developed by the Mayo Clinic team for the 2011 I2B2/VA/Cincinnati Natural Language Processing (NLP) Challenge. The sentiment classification task is to assign any pertinent emotion to each sentence in suicide notes. We have implemented three systems that have been trained on suicide notes provided by the I2B2 challenge organizer-a machine learning system, a rule-based system, and a system consisting of a combination of both. Our machine learning system was trained on re-annotated data in which apparently inconsistent emotion assignment was adjusted. Then, the machine learning methods by RIPPER and multinomial Naïve Bayes classifiers, manual pattern matching rules, and the combination of the two systems were tested to determine the emotions within sentences. The combination of the machine learning and rule-based system performed best and produced a micro-average F-score of 0.5640.

Keywords: machine learning; natural language processing; sentiment classification; suicidal emotion.

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Figures

Figure 1.
Figure 1.
Statistics of 600 suicide notes in the training set.
Figure 2.
Figure 2.
Summary of pattern matching rules.
See this image and copyright information in PMC

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