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Sentiment and Factual Transitions in Online Medical Forums

Bobicev, Victoria
; 
Sokolova, Marina
; 
Oakes, Michael
Authors
Bobicev, Victoria
 Sokolova, Marina
 Oakes, Michael
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Issue Date
2015
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Subjects
Sentiment Analysis
 Online Medical Forums
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CanAI2015_Submit.pdf
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Abstract
This work studies sentiment and factual transitions on an online medical forum where users correspond in English. We work with discussions dedicated to reproductive technologies, an emotionally-charged issue. In several learning problems, we demonstrate that multi-class sentiment classification significantly improves when messages are represented by affective terms combined with sentiment and factual transition information (paired t-test, P=0.0011).
Citation
Publisher
Springer
Journal
Advances in Artificial Intelligence, 28th Canadian Conference on Artificial Intelligence
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URI
http://hdl.handle.net/2436/620981
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Type
Journal article
Language
en
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ISBN
978-3-319-18356-5
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Self-funded
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Faculty of Social Sciences