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Presidential preferences in Colombia through Sentiment Analysis
| dc.contributor.author | Puertas, Edwin | |
| dc.contributor.author | Martinez-Santos, Juan Carlos | |
| dc.contributor.author | Pertuz-Duran, Pablo Andres | |
| dc.date.accessioned | 2023-07-21T16:19:15Z | |
| dc.date.available | 2023-07-21T16:19:15Z | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2023-07 | |
| dc.identifier.citation | Puertas, 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/ANDESCON56260.2022.9989700 | spa |
| dc.identifier.uri | https://hdl.handle.net/20.500.12585/12314 | |
| dc.description.abstract | This 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.mimetype | application/pdf | spa |
| dc.language.iso | eng | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.source | 2022 IEEE ANDESCON: Technology and Innovation for Andean Industry, ANDESCON 2022 | spa |
| dc.title | Presidential preferences in Colombia through Sentiment Analysis | spa |
| dcterms.bibliographicCitation | Alva-Segura, D.A. (2021) Análisis Del Sentimiento Político en Twitter Durante Las Elecciones Congresales 2020 en El Perú Master's thesis | spa |
| dcterms.bibliographicCitation | Cuervo, 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.bibliographicCitation | Andriot, 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_34 | spa |
| dcterms.bibliographicCitation | Boiy, E., Hens, P., Deschacht, K., Moens, M.-F. Automatic sentiment analysis in on-line text (2007) Openness in Digital Publishing: Awareness, Discovery and Access - Proceedings of the 11th International Conference on Electronic Publishing, ELPUB 2007, pp. 349-360. Cited 144 times. ISBN: 978-385437292-9 | spa |
| dcterms.bibliographicCitation | Naiknaware, 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.2018.8653602 | spa |
| dcterms.bibliographicCitation | Abdullah, 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.bibliographicCitation | Puertas, 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/s12559-021-09818-9 | spa |
| dcterms.bibliographicCitation | Moreno-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.bibliographicCitation | Puertas, E., Martinez-Santos, J.C. (2021) Phonetic Detection for Hate Speech Spreaders on Twitter. Cited 3 times. | spa |
| dcterms.bibliographicCitation | Smagulova, K., James, A.P. A survey on LSTM memristive neural network architectures and applications (2019) European Physical Journal: Special Topics, 228 (10), pp. 2313-2324. Cited 126 times. http://www.springer.com/west/home?SGWID=4-102-70-173670503-0&changeHeader=true doi: 10.1140/epjst/e2019-900046-x | spa |
| dcterms.bibliographicCitation | Chicco, D., Jurman, G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation (Open Access) (2020) BMC Genomics, 21 (1), art. no. 6. Cited 1810 times. http://www.biomedcentral.com/bmcgenomics doi: 10.1186/s12864-019-6413-7 | spa |
| dcterms.bibliographicCitation | Zou, Q., Xie, S., Lin, Z., Wu, M., Ju, Y. Finding the Best Classification Threshold in Imbalanced Classification (Open Access) (2016) Big Data Research, 5, pp. 2-8. Cited 131 times. https://www.journals.elsevier.com/big-data-research doi: 10.1016/j.bdr.2015.12.001 | spa |
| datacite.rights | http://purl.org/coar/access_right/c_abf2 | spa |
| oaire.version | http://purl.org/coar/version/c_b1a7d7d4d402bcce | spa |
| dc.type.driver | info:eu-repo/semantics/article | spa |
| dc.type.hasversion | info:eu-repo/semantics/draft | spa |
| dc.identifier.doi | 10.1109/ANDESCON56260.2022.9989700 | |
| dc.subject.keywords | Language model | spa |
| dc.subject.keywords | Natural language processing | spa |
| dc.subject.keywords | Presidential debate | spa |
| dc.subject.keywords | Sentimental analysis | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.cc | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
| dc.identifier.instname | Universidad Tecnológica de Bolívar | spa |
| dc.identifier.reponame | Repositorio Universidad Tecnológica de Bolívar | spa |
| dc.publisher.place | Cartagena de Indias | spa |
| dc.type.spa | http://purl.org/coar/resource_type/c_6501 | spa |
| oaire.resourcetype | http://purl.org/coar/resource_type/c_6501 | spa |
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Universidad Tecnológica de Bolívar - 2017 Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional. Resolución No 961 del 26 de octubre de 1970 a través de la cual la Gobernación de Bolívar otorga la Personería Jurídica a la Universidad Tecnológica de Bolívar.
