Statistical and Similarity Methods for Classifying Emotion in Suicide Notes | Zendy

Kirk Roberts | Zendy; Sanda M. Harabagiu | Zendy

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Statistical and Similarity Methods for Classifying Emotion in Suicide Notes

Author(s) -

Kirk Roberts,

Sanda M. Harabagiu

Publication year - 2012

Publication title -

biomedical informatics insights

Language(s) - English

Resource type - Journals

ISSN - 1178-2226

DOI - 10.4137/bii.s8958

Subject(s) - similarity (geometry) , variety (cybernetics) , task (project management) , recall , natural language processing , computer science , artificial intelligence , sentiment analysis , psychology , emotion detection , cognitive psychology , machine learning , emotion recognition , engineering , systems engineering , image (mathematics)

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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