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Sentiment analysis in health related forums

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dc.contributor.author BOBICEV, Victoria
dc.contributor.author SOKOLOVA, Marina
dc.date.accessioned 2019-10-22T10:12:16Z
dc.date.available 2019-10-22T10:12:16Z
dc.date.issued 2014
dc.identifier.citation BOBICEV, Victoria, SOKOLOVA, Marina. Sentiment analysis in health related forums. In: Microelectronics and Computer Science: proc. of the 8th intern. conf., October 22-25, 2014. Chişinău, 2014, pp. 213-216. ISBN 978-9975-45-329-5. en_US
dc.identifier.isbn 978-9975-45-329-5
dc.identifier.uri http://repository.utm.md/handle/5014/4998
dc.description.abstract In this work, we have presented the sentiment analysis of messages posted on medical forums. We stated the sentiment analysis as a multi-class classification problem in which posts were classified into encouragement, gratitude, confusion, facts, facts + encouragement and uncertain categories. We applied the reader-centered manual annotation and achieved a strong agreement between the annotators: Fleiss Kappa = 0.73. We presented an ad-hoc method of the lexicon creation which is comparatively easy to implement. We have shown that the lexicon, which we call HealthAffect, provided the best accuracy in machine learning experiments. . We used two algorithms, NB and KNN, to solve a multi-class sentiment classification problem. The probability-based NB demonstrated a better performance than KNN. en_US
dc.language.iso en en_US
dc.publisher Tehnica UTM 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 computational linguistics en_US
dc.subject natural language processing en_US
dc.subject sentiment analysis en_US
dc.title Sentiment analysis in health related forums en_US
dc.type Article en_US


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