Learning Sentiments from Tweets with Personal Health Information

BOBICEV, Victoria; SOKOLOVA, Marina; JAFER, Yasser; SCHRAMM, David

dc.contributor.author	BOBICEV, Victoria
dc.contributor.author	SOKOLOVA, Marina
dc.contributor.author	JAFER, Yasser
dc.contributor.author	SCHRAMM, David
dc.date.accessioned	2021-04-10T14:35:11Z
dc.date.available	2021-04-10T14:35:11Z
dc.date.issued	2012
dc.identifier.citation	BOBICEV, Victoria, SOKOLOVA, Marina, JAFER, Yasser. Learning Sentiments from Tweets with Personal Health Information. In: Advances in Artificial Intelligence: proc. Canadian AI 2012, 28-30 May, 2012, Toronto, Canada, 2012, V. 7310, pp. 37-48. ISBN 978-3-642-30353-1.	en_US
dc.identifier.isbn	978-3-642-30353-1
dc.identifier.uri	https://doi.org/10.1007/978-3-642-30353-1_4
dc.identifier.uri	http://repository.utm.md/handle/5014/14094
dc.description	Acces full text: https://doi.org/10.1007/978-3-642-30353-1_4	en_US
dc.description.abstract	We present results of sentiment analysis in Twitter messages that disclose personal health information. In these messages (tweets), users discuss ailment, treatment, medications, etc. We use the author-centric annotation model to label tweets as positive sentiments, negative sentiments or neutral. The results of the agreement among three raters are reported and discussed. We then use Machine Learning methods on multi-class and binary classification of sentiments. The obtained results are comparable with previous results in the subjectivity analysis of user-written Web content.	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 analysis	en_US
dc.subject	messages	en_US
dc.subject	tweets	en_US
dc.subject	annotations	en_US
dc.subject	sentiments	en_US
dc.title	Learning Sentiments from Tweets with Personal Health Information	en_US
dc.type	Article	en_US