Confused and Thankful: Multi-label Sentiment Classification of Health Forums

BOBICEV, Victoria; SOKOL, Marina

dc.contributor.author	BOBICEV, Victoria
dc.contributor.author	SOKOL, Marina
dc.date.accessioned	2021-04-10T13:13:50Z
dc.date.available	2021-04-10T13:13:50Z
dc.date.issued	2017
dc.identifier.citation	BOBICEV, Victoria, SOKOL, Marina. Confused and Thankful: Multi-label Sentiment Classification of Health Forums. In: Advances in Artificial Intelligence: proc. Canadian AI 2017, 16-19 May, 2017, Edmonton, Canada, 2017, V. 10233, pp. 284-289. ISBN 978-3-319-57351-9.	en_US
dc.identifier.isbn	978-3-319-57351-9
dc.identifier.uri	https://doi.org/10.1007/978-3-319-57351-9_33
dc.identifier.uri	http://repository.utm.md/handle/5014/14090
dc.description	Acces full text: https://doi.org/10.1007/978-3-319-57351-9_33	en_US
dc.description.abstract	Our current work studies sentiment representation in messages posted on health forums. We analyze 11 sentiment representations in a framework of multi-label learning. We use Exact Match and F-score to compare effectiveness of those representations in sentiment classification of a message. Our empirical results show that feature selection can significantly improve Exact Match of the multi-label sentiment classification (paired t-test, P = 0.0024).	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	sentiment classification	en_US
dc.subject	multi-label learning	en_US
dc.subject	learning	en_US
dc.subject	medical forums	en_US
dc.subject	forums	en_US
dc.title	Confused and Thankful: Multi-label Sentiment Classification of Health Forums	en_US
dc.type	Article	en_US