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

Victoria Bobicev, Marina Sokolova, Michael Oakes
2015 Cognitive Computation  
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 language and online discussions of personal health. In this work, we study sentiments
more » ... d 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 sequence and separate posts respectively). The annotated posts were used to analyse sentiments in consecutive posts. In four multi-class classification problems, we assessed HealthAffect, a domain-specific affective lexicon, as well general sentiment lexicons in their ability to represent messages in sentiment recognition.
doi:10.1007/s12559-015-9327-y fatcat:3xclttmi2nf5fbmtjpnxqvw2eq