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A hybrid model for automatic emotion recognition in suicide notes

Yang, Hui; Willis, Alistair; De Roeck, Anne and Nuseibeh, Bashar (2012). A hybrid model for automatic emotion recognition in suicide notes. Biomedical Informatics Insights, 5(Supp 1) pp. 17–30.

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DOI (Digital Object Identifier) Link: http://doi.org/10.4137/BII.S8948
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Abstract

We describe the Open University team’s submission to the 2011 i2b2/VA/Cincinnati Medical Natural Language Processing Challenge, Track 2 Shared Task for sentiment analysis in suicide notes. This Shared Task focused on the development of automatic systems that identify, at the sentence level, affective text of 15 specific emotions from suicide notes. We propose a hybrid model that incorporates a number of natural language processing techniques, including lexicon-based keyword spotting, CRF-based emotion cue identification, and machine learning-based emotion classification. The results generated by different techniques are integrated using different vote-based merging strategies. The automated system performed well against the manually-annotated gold standard, and achieved encouraging results with a micro-averaged F-measure score of 61.39% in textual emotion recognition, which was ranked 1st place out of 24 participant teams in this challenge. The results demonstrate that effective emotion recognition by an automated system is possible when a large annotated corpus is available.

Item Type: Journal Article
Copyright Holders: 2012 The Authors
ISSN: 1178-2226
Keywords: emotion recognition; keyword-based model; machine-learning-based model; hybrid model; result integration
Academic Unit/Department: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 31584
Depositing User: Alistair Willis
Date Deposited: 17 Jan 2012 11:35
Last Modified: 23 Sep 2016 10:00
URI: http://oro.open.ac.uk/id/eprint/31584
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