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dc.contributor.authorŽunić, Anastaziaen_US
dc.contributor.authorCorcoran, Padraigen_US
dc.contributor.authorSpasić, Irenaen_US
dc.date.accessioned2023-10-10T10:25:52Z-
dc.date.available2023-10-10T10:25:52Z-
dc.date.issued2020-
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/5170-
dc.description.abstractSentiment analysis is a natural language processing task that aims to automatically classify the sentiment expressed in text. In this study, we compare the performance of five publicly available sentiment analysis tools along with the ensemble method that combines them. Their performance was evaluated on two datasets, which represent user-generated content. One of these, namely drug reviews, is related to health and wellbeing. The second one, movie reviews, is used for cross-domain comparison of sentiment analysis. Explicit domain knowledge formally modelled by the Unified Medical Language System was used for semantic enrichment to investigate whether it can improve the performance of the sentiment analysis tools considered by reducing the bias towards the negative sentiment. Our experiments demonstrated an improvement in F-score by 7 percent points.en_US
dc.titleImproving the performance of sentiment analysis in health and wellbeing using domain knowledgeen_US
dc.typeConference Paperen_US
dc.relation.conferenceThird UK Healthcare Text Analytics Conference (HealTAC), London, UK, 22-24 April 2020en_US
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeConference Paper-
item.grantfulltextnone-
crisitem.author.orcid0000-0001-5222-1268-
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