Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)Cables Pérez, Elio HiginioNeira Espitia, Edison LeonardoArias García, Héctor LeonardoDoria Pérez, Luís Carlos2021-11-032021-11-032021-06-03http://repositorio.uan.edu.co/handle/123456789/5160In this degree work, a sentiment analysis of the perception of COVID-19 vaccination in Colombia is carried out, taking as a source of data the publications on the social network Twitter. With the activities carried out, it was possible to obtain information on the Tweets from March 15 to April 25, 2021 through the streaming API provided by the social network, the information was stored in MongoDB databases in the cloud. Python was used as a programming language for the implementation of the source code by creating notebooks.En el presente trabajo de grado se realiza un análisis de sentimiento de la percepción de la vacunación del COVID-19 en Colombia, tomando como fuente de datos las publicaciones en la red social Twitter. Con las actividades realizadas se logró obtener información de los Tweets desde el día 15 de marzo al día 25 de abril del año 2021 por medio de la API streaming proporcionada por la red social, se almaceno la información en bases de datos MongoDB en la nube. Se utilizó Python como lenguaje de programación para la implementación del código fuente mediante la creación de notebooks.spaAcceso abiertoAnálisis de sentimientosMinería de textoCOVID-19Trabajo de grado (Pregrado y/o Especialización)Sentiment analysisText miningCOVID-19info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Alamoodi, A., Zaidan, B., Zaidan, A., Albahri, O., Mohammed, K., Malik, R., Almahdi, E., Chyad, M., Tareq, Z., Albahri, A., Hameed, H., & Alaa, M. (2021). Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review. 13Bian, Y., Cui, K., Wang, L., Zheng, G., Guo, H., Yang, J., Jiang, M., & Lu, A. (2014)IEEE International Conference on Bioinformatics and Biomedicine - Application of Acupuncture on Coronary Heart Disease Treatment: A Text Mining Study. 4. Bian, Y., Zhou, H., Guo, J., Wang, Y., Zheng, G., Guo, H., Tan, Y., Ren, X., Dong, R., Zhang, J., Cui, Z., Lu, A., Jiang, M., & Wang, Y. (2014). IEEE International Conference on Bioinformatics and Biomedicine, Study of acupuncture therapy on hypertension based on text ming. 4.Bisong, E. (2019). Building Machine Learning and Deep Learning Models on Google Cloud Platform.Caputo, A., Giacchetta, A., & Langher, V. (2016). AIDS as social construction: text mining of AIDSrelated information in the Italian press. 7Chakraborty, K., Bhatia, S., Bhattacharyya, S., Platos, J., Bag, R., & Hassanien, A. (2020). Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media. ELSEVIER, 14.Gonzalez Peña, D., Lourenço, A., López Fernández, H., Reboiro Jato, H., & Fdez Riverola, F. (2014, SEPTIEMBRE). Web scraping technologies in an API world - Briefings in BioinformaticsKabir, Y., & Madria, S. (2020, JULIO 11). CoronaVis: A Real-time COVID-19 Tweets Data Analyzer and Data Repository. 10.KARAMI, A., LUNDY, M., WEBB, F., & DWIVEDI, Y. (2020). Twitter and Research: A Systematic Literature Review Through Text Mining. IEEE ACCESS.Martinez, J. (2016). Primer Taller de Análisis de Sentimiento en Twitter con R. DB GUIDANCE. https://www.youtube.com/watch?v=nOIZnYLlPBoinstname:Universidad Antonio Nariñoreponame:Repositorio Institucional UANrepourl:https://repositorio.uan.edu.co/