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Presidential preferences in Colombia through Sentiment Analysis

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datacite.rightshttp://purl.org/coar/access_right/c_abf2spa
dc.contributor.authorPertuz-Duran, Pablo Andres
dc.contributor.authorMartínez Santos, Juan Carlos
dc.contributor.authorPuertas Del Castillo, Edwin Alexander
dc.date.accessioned2023-07-21T16:19:15Z
dc.date.available2023-07-21T16:19:15Z
dc.date.issued2022
dc.date.submitted2023-07
dc.description.abstractThis work carries out the sentiment analysis of the social network Twitter regarding the presidential debate on May 23, where a hashtag was left open so viewers could give their points of view on these three candidates: Gustavo Petro, Federico Gutierrez, and Rodolfo Hernández. Once we extracted these Tweets contained in the hashtag, they were manually classified. They then went through all the pre-processing and elimination of special characters, links, URLs, images, or videos. Next, the TextVectorization layer from the TensorFlow library was used to convert these tweets to vectors and finally to go through the two models. The results show the best results for the BERT model with an accuracy of 76% and an F1 score of 85%.spa
dc.format.mimetypeapplication/pdfspa
dc.identifier.citationPuertas, E., Martinez-Santos, J.C., Andres Pertuz-Duran, P. Presidential preferences in Colombia through Sentiment Analysis (2022) 2022 IEEE ANDESCON: Technology and Innovation for Andean Industry, ANDESCON 2022, . DOI: 10.1109/ANDESCON56260.2022.9989700spa
dc.identifier.doi10.1109/ANDESCON56260.2022.9989700
dc.identifier.instnameUniversidad Tecnológica de Bolívarspa
dc.identifier.reponameRepositorio Universidad Tecnológica de Bolívarspa
dc.identifier.urihttps://hdl.handle.net/20.500.12585/12314
dc.language.isoengspa
dc.publisher.placeCartagena de Indiasspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.source2022 IEEE ANDESCON: Technology and Innovation for Andean Industry, ANDESCON 2022spa
dc.subject.keywordsLanguage modelspa
dc.subject.keywordsNatural language processingspa
dc.subject.keywordsPresidential debatespa
dc.subject.keywordsSentimental analysisspa
dc.titlePresidential preferences in Colombia through Sentiment Analysisspa
dc.typeArtículo de revistaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.hasversioninfo:eu-repo/semantics/draftspa
dcterms.bibliographicCitationAlva-Segura, D.A. (2021) Análisis Del Sentimiento Político en Twitter Durante Las Elecciones Congresales 2020 en El Perú Master's thesisspa
dcterms.bibliographicCitationCuervo, M.C., Guerrero, M.A.V. Predicción electoral usando un modelo híbrido basado en análisis sentimental: Elecciones presidenciales de Colombia (2019) Revista Politécnica, 15 (30), pp. 94-104.spa
dcterms.bibliographicCitationAndriot, J., Park, B., Francia, P., Gudivada, V.N. Sentiment analysis of democratic presidential primaries debate tweets using machine learning models (2020) Advances in Intelligent Systems and Computing, 1155, pp. 339-349. http://www.springer.com/series/11156 ISBN: 978-981154028-8 doi: 10.1007/978-981-15-4029-5_34spa
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dcterms.bibliographicCitationNaiknaware, B.R., Kawathekar, S.S. Prediction of 2019 Indian Election using sentiment analysis (2019) Proceedings of the International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2018, art. no. 8653602, pp. 660-665. Cited 8 times. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8651203 ISBN: 978-153861442-6 doi: 10.1109/I-SMAC.2018.8653602spa
dcterms.bibliographicCitationAbdullah, M., Hadzikadic, M. (2017) Sentiment Analysis of Twitter Data: Emotions Revealed Regarding Donald Trump during the 2015-16 Primary Debates., pp. 760-764.spa
dcterms.bibliographicCitationPuertas, E., Moreno-Sandoval, L.G., Redondo, J., Alvarado-Valencia, J.A., Pomares-Quimbaya, A. Detection of Sociolinguistic Features in Digital Social Networks for the Detection of Communities (2021) Cognitive Computation, 13 (2), pp. 518-537. Cited 4 times. http://www.springer.com/biomed/neuroscience/journal/12559 doi: 10.1007/s12559-021-09818-9spa
dcterms.bibliographicCitationMoreno-Sandoval, L.G., Del Castillo, E.A.P., Quimbaya, A.P., Alvarado-Valencia, J.A. Assembly of polarity, emotion and user statistics for detection of fake profiles (2020) CLEF (Working Notes). Cited 3 times.spa
dcterms.bibliographicCitationPuertas, E., Martinez-Santos, J.C. (2021) Phonetic Detection for Hate Speech Spreaders on Twitter. Cited 3 times.spa
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oaire.versionhttp://purl.org/coar/version/c_b1a7d7d4d402bccespa
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