Yang, H., Willis, A., de Roeck, A. et al. (1 more author) (2012) A Hybrid Model for Automatic Emotion Recognition in Suicide Notes. Biomedical Informatics Insights, 2012:5 (Suppl 1). pp. 17-30. ISSN 1178-2226
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © the author(s), publisher and licensee Libertas Academica Ltd. This is an open access article. Unrestricted non-commercial use is permitted provided the original work is properly cited. |
Keywords: | emotion recognition; keyword-based model; machine-learning-based model; hybrid model; result integration |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 12 Dec 2016 14:38 |
Last Modified: | 12 Dec 2016 14:39 |
Published Version: | http://dx.doi.org/10.4137/BII.S8948 |
Status: | Published |
Publisher: | Libertas Academica |
Refereed: | Yes |
Identification Number: | 10.4137/BII.S8948 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:108937 |