What Goes Around Comes Around: Learning Sentiments in Online Medical Forums

2.50
Hdl Handle:
http://hdl.handle.net/2436/620917
Title:
What Goes Around Comes Around: Learning Sentiments in Online Medical Forums
Authors:
Bobicev, Victoria; Sokolova, Marina; Oakes, Michael
Abstract:
Currently 19%-28% of Internet users participate in online health discussions. A 2011 survey of the US population estimated that 59% of all adults have looked online for information about health topics such as a specific disease or treatment. Although empirical evidence strongly supports the importance of emotions in health-related messages, there are few studies of the relationship between a subjective lan-guage and online discussions of personal health. In this work, we study sentiments expressed on online medical forums. As well as considering the predominant sentiments expressed in individual posts, we analyze sequences of sentiments in online discussions. Individual posts are classified into one of five categories. We identified three categories as sentimental (encouragement, gratitude, confusion) and two categories as neutral (facts, endorsement). 1438 messages from 130 threads were annotated manually by two annotators with a strong inter-annotator agreement (Fleiss kappa = 0.737 and 0.763 for posts in se-quence and separate posts respectively). The annotated posts were used to analyse sentiments in consec-utive posts. In four multi-class classification problems, we assessed HealthAffect, a domain-specific af-fective lexicon, as well general sentiment lexicons in their ability to represent messages in sentiment recognition.
Citation:
What Goes Around Comes Around: Learning Sentiments in Online Medical Forums 2015, 7 (5):609 Cognitive Computation
Publisher:
Springer
Journal:
Cognitive Computation
Issue Date:
2-Apr-2015
URI:
http://hdl.handle.net/2436/620917
DOI:
10.1007/s12559-015-9327-y
Additional Links:
http://link.springer.com/10.1007/s12559-015-9327-y
Type:
Article
Language:
en
ISSN:
1866-9956; 1866-9964
Appears in Collections:
FOSS

Full metadata record

DC FieldValue Language
dc.contributor.authorBobicev, Victoriaen
dc.contributor.authorSokolova, Marinaen
dc.contributor.authorOakes, Michaelen
dc.date.accessioned2017-11-29T10:09:31Z-
dc.date.available2017-11-29T10:09:31Z-
dc.date.issued2015-04-02-
dc.identifier.citationWhat Goes Around Comes Around: Learning Sentiments in Online Medical Forums 2015, 7 (5):609 Cognitive Computationen
dc.identifier.issn1866-9956-
dc.identifier.issn1866-9964-
dc.identifier.doi10.1007/s12559-015-9327-y-
dc.identifier.urihttp://hdl.handle.net/2436/620917-
dc.description.abstractCurrently 19%-28% of Internet users participate in online health discussions. A 2011 survey of the US population estimated that 59% of all adults have looked online for information about health topics such as a specific disease or treatment. Although empirical evidence strongly supports the importance of emotions in health-related messages, there are few studies of the relationship between a subjective lan-guage and online discussions of personal health. In this work, we study sentiments expressed on online medical forums. As well as considering the predominant sentiments expressed in individual posts, we analyze sequences of sentiments in online discussions. Individual posts are classified into one of five categories. We identified three categories as sentimental (encouragement, gratitude, confusion) and two categories as neutral (facts, endorsement). 1438 messages from 130 threads were annotated manually by two annotators with a strong inter-annotator agreement (Fleiss kappa = 0.737 and 0.763 for posts in se-quence and separate posts respectively). The annotated posts were used to analyse sentiments in consec-utive posts. In four multi-class classification problems, we assessed HealthAffect, a domain-specific af-fective lexicon, as well general sentiment lexicons in their ability to represent messages in sentiment recognition.en
dc.language.isoenen
dc.publisherSpringeren
dc.relation.urlhttp://link.springer.com/10.1007/s12559-015-9327-yen
dc.rightsArchived with thanks to Cognitive Computationen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectSentiment Analysisen
dc.subjectOnline Medical Forumsen
dc.titleWhat Goes Around Comes Around: Learning Sentiments in Online Medical Forumsen
dc.typeArticleen
dc.identifier.journalCognitive Computationen
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