Statistical and Similarity Methods for Classifying Emotion in Suicide Notes

Kirk Roberts; Sanda M. Harabagiu

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Biomedical Informatics Insights

Statistical and Similarity Methods for Classifying Emotion in Suicide Notes

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Kirk Roberts and Sanda M. Harabagiu

Biomedical Informatics Insights 2012:5 (Suppl. 1) 195-204

Original Research

Published on 30 Jan 2012

DOI: 10.4137/BII.S8958

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Abstract

In this paper we report on the approaches that we developed for the 2011 i2b2 Shared Task on Sentiment Analysis of Suicide Notes. We have cast the problem of detecting emotions in suicide notes as a supervised multi-label classification problem. Our classifiers use a variety of features based on (a) lexical indicators, (b) topic scores, and (c) similarity measures. Our best submission has a precision of 0.551, a recall of 0.485, and a F-measure of 0.516.

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