Publicação
Predicting an election’s outcome using sentiment analysis
| Resumo: | Political debate - in its essence - carries a robust emotional charge, and social media have become a vast arena for voters to disseminate and discuss the ideas proposed by candidates. The Brazilian presidential elections of 2018 were marked by a high level of polarization, making the discussion of the candidates’ ideas an ideological battlefield, full of accusations and verbal aggression, creating an excellent source for sentiment analysis. In this paper, we analyze the emotions of the tweets posted about the presidential candidates of Brazil on Twitter, so that it was possible to identify the emotional profile of the adherents of each of the leading candidates, and thus to discern which emotions had the strongest effects upon the election results. Also, we created a model using sentiment analysis and machine learning, which predicted with a correlation of 0.90 the final result of the election. |
|---|---|
| Autores principais: | Martins, Ricardo |
| Outros Autores: | Almeida, J. J.; Henriques, Pedro Rangel; Novais, Paulo |
| Assunto: | Emotion analysis Machine learning Natural processing language Sentiment analysis Ciências Naturais::Ciências da Computação e da Informação |
| Ano: | 2020 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| _version_ | 1867439373945929728 |
|---|---|
| author | Martins, Ricardo |
| author2 | Almeida, J. J. Henriques, Pedro Rangel Novais, Paulo |
| author2_role | author author author |
| author_facet | Martins, Ricardo Almeida, J. J. Henriques, Pedro Rangel Novais, Paulo |
| author_role | author |
| contributor_name_str_mv | RepositóriUM - Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Martins, Ricardo\"},{\"Person.name\":\"Almeida, J. J.\"},{\"Person.name\":\"Henriques, Pedro Rangel\"},{\"Person.name\":\"Novais, Paulo\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | RepositóriUM - Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | Martins, Ricardo Almeida, J. J. Henriques, Pedro Rangel Novais, Paulo |
| datacite.date.Accepted.fl_str_mv | 2020-07-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2021-01-14T12:15:30Z |
| datacite.date.embargoed.fl_str_mv | 2021-01-14T12:15:30Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Emotion analysis Machine learning Natural processing language Sentiment analysis Ciências Naturais::Ciências da Computação e da Informação |
| datacite.titles.title.fl_str_mv | Predicting an election’s outcome using sentiment analysis |
| dc.contributor.none.fl_str_mv | RepositóriUM - Universidade do Minho |
| dc.creator.none.fl_str_mv | Martins, Ricardo Almeida, J. J. Henriques, Pedro Rangel Novais, Paulo |
| dc.date.Accepted.fl_str_mv | 2020-07-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2021-01-14T12:15:30Z |
| dc.date.embargoed.fl_str_mv | 2021-01-14T12:15:30Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/69230 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Springer |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Emotion analysis Machine learning Natural processing language Sentiment analysis Ciências Naturais::Ciências da Computação e da Informação |
| dc.title.fl_str_mv | Predicting an election’s outcome using sentiment analysis |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | Political debate - in its essence - carries a robust emotional charge, and social media have become a vast arena for voters to disseminate and discuss the ideas proposed by candidates. The Brazilian presidential elections of 2018 were marked by a high level of polarization, making the discussion of the candidates’ ideas an ideological battlefield, full of accusations and verbal aggression, creating an excellent source for sentiment analysis. In this paper, we analyze the emotions of the tweets posted about the presidential candidates of Brazil on Twitter, so that it was possible to identify the emotional profile of the adherents of each of the leading candidates, and thus to discern which emotions had the strongest effects upon the election results. Also, we created a model using sentiment analysis and machine learning, which predicted with a correlation of 0.90 the final result of the election. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | conferencePaper |
| fulltext.url.fl_str_mv | https://repositorium.uminho.pt/bitstreams/b014d943-4019-4306-ae54-88afc579c828/download |
| id | rum_0dfbd1d78d2bcdfe1b2828eadf807b63 |
| identifier.url.fl_str_mv | https://hdl.handle.net/1822/69230 |
| instacron_str | repositorium |
| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
| network_acronym_str | rum |
| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/69230 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | Martins, Ricardo Almeida, J. J. Henriques, Pedro Rangel Novais, Paulo |
| publishDate | 2020 |
| publisher.none.fl_str_mv | Springer |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
| service_str_mv | urn:repositoryAcronym:rum |
| spelling | engSpringerporPolitical debate - in its essence - carries a robust emotional charge, and social media have become a vast arena for voters to disseminate and discuss the ideas proposed by candidates. The Brazilian presidential elections of 2018 were marked by a high level of polarization, making the discussion of the candidates’ ideas an ideological battlefield, full of accusations and verbal aggression, creating an excellent source for sentiment analysis. In this paper, we analyze the emotions of the tweets posted about the presidential candidates of Brazil on Twitter, so that it was possible to identify the emotional profile of the adherents of each of the leading candidates, and thus to discern which emotions had the strongest effects upon the election results. Also, we created a model using sentiment analysis and machine learning, which predicted with a correlation of 0.90 the final result of the election.application/pdfporPredicting an election’s outcome using sentiment analysisMartins, RicardoAlmeida, J. J.Henriques, Pedro RangelNovais, PauloHostingInstitutionOrganizationalRepositóriUM - Universidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptCITATIONMartins, R., Almeida, J., Henriques, P., & Novais, P. (2020, April). Predicting an Election’s Outcome Using Sentiment Analysis. In World Conference on Information Systems and Technologies (pp. 134-143). SpringerISBNIsPartOf978-3-030-45687-0ISSNIsPartOf2194-5357DOIIsPartOf10.1007/978-3-030-45688-7_14EISBNIsPartOf978-3-030-45688-72021-01-14T12:15:30Z2020-07-012020-12-30T20:08:45Z2020-07-01T00:00:00ZHandlehttps://hdl.handle.net/1822/69230http://purl.org/coar/access_right/c_abf2open accessEmotion analysisMachine learningNatural processing languageSentiment analysishttp://www.oecd.org/science/inno/38235147.pdfFields of Science and Technology (FOS)Ciências Naturais::Ciências da Computação e da Informação175670 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paperhttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorium.uminho.pt/bitstreams/b014d943-4019-4306-ae54-88afc579c828/download |
| spellingShingle | Predicting an election’s outcome using sentiment analysis Martins, Ricardo Emotion analysis Machine learning Natural processing language Sentiment analysis Ciências Naturais::Ciências da Computação e da Informação |
| status | SINGLETON |
| subject.fl_str_mv | Emotion analysis Machine learning Natural processing language Sentiment analysis |
| subject.other.fl_str_mv | Ciências Naturais::Ciências da Computação e da Informação |
| title | Predicting an election’s outcome using sentiment analysis |
| title_full | Predicting an election’s outcome using sentiment analysis |
| title_fullStr | Predicting an election’s outcome using sentiment analysis |
| title_full_unstemmed | Predicting an election’s outcome using sentiment analysis |
| title_short | Predicting an election’s outcome using sentiment analysis |
| title_sort | Predicting an election’s outcome using sentiment analysis |
| topic | Emotion analysis Machine learning Natural processing language Sentiment analysis Ciências Naturais::Ciências da Computação e da Informação |
| topic_facet | Emotion analysis Machine learning Natural processing language Sentiment analysis Ciências Naturais::Ciências da Computação e da Informação |
| url | https://hdl.handle.net/1822/69230 |
| visible | 1 |