Combining sentiment analysis scores to improve accuracy of polarity classification in MOOC posts

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorLaroca, Herbert-
Autor(es): dc.creatorRocio, Vitor-
Data de aceite: dc.date.accessioned2020-09-24T17:27:02Z-
Data de disponibilização: dc.date.available2020-09-24T17:27:02Z-
Data de envio: dc.date.issued2019-11-03-
Data de envio: dc.date.issued2019-11-03-
Data de envio: dc.date.issued2019-09-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/10400.2/8703-
Fonte completa do material: dc.identifierhttps://doi.org/10.1007/978-3-030-30241-2_4-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/10400.2/8703-
Descrição: dc.descriptionSentiment analysis is a set of techniques that deal with the verification of sentiment and emotions in written texts. This introductory work aims to explore the combination of scores and polarities of sentiments (positive, neutral and negative) provided by different sentiment analysis tools. The goal is to generate a final score and its respective polarity from the normalization and arithmetic average scores given by those tools that provide a minimum of reliability. The texts analyzed to test our hypotheses were obtained from forum posts from participants in a massive open online course (MOOC) offered by Universidade Aberta de Portugal, and were submitted to four online service APIs offering sentiment analysis: Amazon Comprehend, Google Natural Language, IBM Watson Natural Language Understanding, and Microsoft Text Analytics. The initial results are encouraging, suggesting that the average score is a valid way to increase the accuracy of the predictions from different sentiment analyzers.-
Descrição: dc.descriptioninfo:eu-repo/semantics/publishedVersion-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer International Publishing-
Direitos: dc.rightsrestrictedAccess-
Direitos: dc.rightshttp://creativecommons.org/licenses/by/4.0/-
Palavras-chave: dc.subjectSentiment analysis-
Palavras-chave: dc.subjectMassive open online course-
Palavras-chave: dc.subjectPolarity scores-
Palavras-chave: dc.subjectODS::04:Educação de Qualidade-
Título: dc.titleCombining sentiment analysis scores to improve accuracy of polarity classification in MOOC posts-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Aberto - Universidade Aberta (Portugal)

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