Presidential preferences in Colombia through Sentiment Analysis
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 thes...
- Autores:
-
Puertas, Edwin
Martinez-Santos, Juan Carlos
Pertuz-Duran, Pablo Andres
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12314
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12314
- Palabra clave:
- Language model
Natural language processing
Presidential debate
Sentimental analysis
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Presidential preferences in Colombia through Sentiment Analysis |
| title |
Presidential preferences in Colombia through Sentiment Analysis |
| spellingShingle |
Presidential preferences in Colombia through Sentiment Analysis Language model Natural language processing Presidential debate Sentimental analysis |
| title_short |
Presidential preferences in Colombia through Sentiment Analysis |
| title_full |
Presidential preferences in Colombia through Sentiment Analysis |
| title_fullStr |
Presidential preferences in Colombia through Sentiment Analysis |
| title_full_unstemmed |
Presidential preferences in Colombia through Sentiment Analysis |
| title_sort |
Presidential preferences in Colombia through Sentiment Analysis |
| dc.creator.fl_str_mv |
Puertas, Edwin Martinez-Santos, Juan Carlos Pertuz-Duran, Pablo Andres |
| dc.contributor.author.none.fl_str_mv |
Puertas, Edwin Martinez-Santos, Juan Carlos Pertuz-Duran, Pablo Andres |
| dc.subject.keywords.spa.fl_str_mv |
Language model Natural language processing Presidential debate Sentimental analysis |
| topic |
Language model Natural language processing Presidential debate Sentimental analysis |
| description |
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%. |
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2022 |
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2022 |
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2023-07-21T16:19:15Z |
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2023-07-21T16:19:15Z |
| dc.date.submitted.none.fl_str_mv |
2023-07 |
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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 |
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https://hdl.handle.net/20.500.12585/12314 |
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10.1109/ANDESCON56260.2022.9989700 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
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 10.1109/ANDESCON56260.2022.9989700 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12314 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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openAccess |
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Cartagena de Indias |
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2022 IEEE ANDESCON: Technology and Innovation for Andean Industry, ANDESCON 2022 |
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Puertas, Edwin5a1b1566-e112-43dc-8ac7-310ea9af8f05Martinez-Santos, Juan Carlos5c958644-c78d-401d-8ba9-bbd39fe77318Pertuz-Duran, Pablo Andres5663e899-784c-4a77-97c0-600d946774cf2023-07-21T16:19:15Z2023-07-21T16:19:15Z20222023-07Puertas, 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/ANDESCON56260.2022.9989700https://hdl.handle.net/20.500.12585/1231410.1109/ANDESCON56260.2022.9989700Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThis 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%.application/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf22022 IEEE ANDESCON: Technology and Innovation for Andean Industry, ANDESCON 2022Presidential preferences in Colombia through Sentiment Analysisinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Language modelNatural language processingPresidential debateSentimental analysisCartagena de IndiasAlva-Segura, D.A. (2021) Análisis Del Sentimiento Político en Twitter Durante Las Elecciones Congresales 2020 en El Perú Master's thesisCuervo, 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.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_34Boiy, 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: 978-385437292-9Naiknaware, 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.2018.8653602Abdullah, M., Hadzikadic, M. (2017) Sentiment Analysis of Twitter Data: Emotions Revealed Regarding Donald Trump during the 2015-16 Primary Debates., pp. 760-764.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/s12559-021-09818-9Moreno-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.Puertas, E., Martinez-Santos, J.C. (2021) Phonetic Detection for Hate Speech Spreaders on Twitter. Cited 3 times.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-xChicco, 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/s12864-019-6413-7Zou, 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. 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