Sentiment and Factual Transitions in Online Medical Forums

BOBICEV, Victoria; SOKOLOVA, Marina; OAKES, Michael

dc.contributor.author	BOBICEV, Victoria
dc.contributor.author	SOKOLOVA, Marina
dc.contributor.author	OAKES, Michael
dc.date.accessioned	2021-04-30T09:38:36Z
dc.date.available	2021-04-30T09:38:36Z
dc.date.issued	2015
dc.identifier.citation	BOBICEV, Victoria, SOKOLOVA, Marina, OAKES, Michael. Sentiment and Factual Transitions in Online Medical Forums. In: Advances in Artificial Intelligence. Canadian AI 2015. Lecture Notes in Computer Science, 2015, V. 909, pp. 204-211. ISBN 978-3-319-18356-5.	en_US
dc.identifier.isbn	978-3-319-18356-5
dc.identifier.uri	https://doi.org/10.1007/978-3-319-18356-5_18
dc.identifier.uri	http://81.180.74.21:8080/xmlui/handle/123456789/14644
dc.description	Acces full text: https://doi.org/10.1007/978-3-319-18356-5_18	en_US
dc.description.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).1.	en_US
dc.language.iso	en	en_US
dc.publisher	Springer Nature Switzerland	en_US
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 United States	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject	medical forums	en_US
dc.subject	forums	en_US
dc.subject	discussions	en_US
dc.title	Sentiment and Factual Transitions in Online Medical Forums	en_US
dc.type	Article	en_US