Aislamiento social obligatorio: un análisis de sentimientos mediante machine learning

Mandatory social isolation: a sentiment analysis using machine learning

Carlos Alberto Arango Pastrana y Carlos Fernando Osorio Andrade

Suma de Negocios, 12(26), 1-13, enero-junio 2021, ISSN 2215-910X

http://doi.org/10.14349/sumneg/2021.V12.N26.A1

Recibido el 5 de noviembre de 2020
Aceptado el 15 de diciembre de 2020
Online el 29 de enero de 2021

Resumen

Para reducir la tasa de contagio por COVID-19, el Gobierno colombiano ha adoptado, entre otras medidas, el aislamiento obligatorio. Esta medida ha generado opiniones divididas, pues a pesar de que ayuda a disminuir la propagación del virus, genera problemas mentales y económicos difíciles de sortear. El objetivo de este documento es analizar los sentimientos subyacentes de los comentarios de Twitter relacionados con el aislamiento, identificando los temas y palabras más frecuentemente utilizados en este contexto. Se construyó un algoritmo de machine learning para identificar los sentimientos de 72 564 publicaciones, y se aplicó un análisis de redes sociales para identificar los temas más frecuentes en los conjuntos de datos. Los resultados sugieren que el algoritmo presenta gran precisión para clasificar sentimientos. Asimismo, a medida que se extiende el aislamiento, los comentarios relacionados con la cuarentena crecen de manera proporcional. Se identificó al miedo como el sentimiento predominante durante todo el periodo de confinamiento en Colombia.


Palabras clave:
Aislamiento obligatorio,
redes sociales,
análisis de sentimientos,
machine learning,
COVID-19.

Códigos JEL:
Z13, B23, H83, I18

Abstract

To reduce the rate of contagion by Covid-19, the Colombian government has adopted, among other measures, for mandatory isolation, with divided opinions, because despite helping to reduce the spread of the virus, it generates mental and economic problems that are difficult to overcome. The objective of this document was to analyze the underlying sentiments in the Twitter comments related to isolation, identifying the topics and words most frequently used in this context. A machine learning algorithm was built to identify sentiments in 72,564 posts and a social network analysis was applied establishing the most frequent topics in the data sets. The results suggest that the algorithm is highly accurate in classifying feelings. Also, as the isolation extends, comments related to the quarantine grow proportionally. Fear was identified as the predominant feeling throughout the period of confinement in Colombia.


Keywords:
Mandatory isolation,
Social networks,
Sentiment analysis,
Machine learning,
COVID-19.

Artículo Completo
Bibliografía

Aballay, L., Aciar, S. & Reategui, E. (2017). Método para detección de emociones desde foros utilizando Text Mining. Campus Virtuales, 6(1), 89-98.

Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E., Gutiérrez, J. & Kochut, K. (2017). A brief survey of text mining: Classification, clustering and extraction techniques. arXiv preprint arXiv:1707.02919. https://bit.ly/3jLuZwD

Atalan, A. (2020). Is the lockdown important to prevent the COVID-19 pandemic? Effects on psychology, environment and economy-perspective. Annals of Medicine and Surgery, 56, 38-42. https://doi.org/10.1016/j.amsu.2020.06.010

Baker, Q., Shatnawi, F., Rawashdeh, S., Al-Smadi, M. & Jararweh, Y. (2020). Detecting epidemic diseases using sentiment analysis of Arabic tweets. Journal of Universal Computer Science, 26(1), 50-70. https://bit.ly/3oPhr6S

Bastian, M., Heymann, S. & Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media, 361-362. https://bit.ly/2TFXcKO

Botes, W. & Thaldar, D. (2020). COVID-19 and quarantine orders: A practical approach. SAMJ: South African Medical Journal, 110(6), 1-4. https://bit.ly/34MwOFx

Cambria, E., Grassi, M., Hussain, A. & Havasi, C. (2012). Sentic Computing for social media marketing. Multimed Tools Appl 59, 557-577. https://doi.org/10.1007/s11042-011-0815-0

Cano, M. & Arce, S. (2020). Análisis de la comunicación en redes sociales de la campaña de la vacuna de gripe en España. Revista Española de Salud Pública, 94(1), 1-10. https://bit.ly/3eeAG4Y

Crokidakis, N. (2020). COVID-19 spreading in Rio de Janeiro, Brazil: Do the policies of social isolation really work? Chaos, Solitons & Fractals, 136, 1-6. https://doi.org/10.1016/j.chaos.2020.109930

Demidova, L. & Klyueva, I. (2017). Improving the classification quality of the SVM classifier for the imbalanced datasets on the base of ideas the SMOTE algorithm. ITM Web of Conferences, 10, 8-11. https://doi.org/10.1051/itmconf/20171002002

Diaz, L. A. & Gutiérrez, E. (2020). La comunicación gubernamental a través de la red social Facebook en tiempos de coronavirus. Análisis del caso de Bahía Blanca, Argentina. GIGAPP Estudios Working Papers, 7(118), 609-626. http://www.gigapp.org/ewp/index.php/GIGAPP-EWP/article/view/223/237

Dinero. (19 de mayo de 2020a). Duque extiende el aislamiento. https://bit.ly/2HWNd0N

Dinero. (28 de mayo de 2020b). Gobierno decretó aislamiento obligatorio hasta el 1 de julio. https://bit.ly/2TQ0rz7

Dinero. (23 de junio de 2020c). Colombia extiende cuarentena hasta el 15 de julio. https://bit.ly/3kPxWgS

Dinero. (7 de julio de 2020d). Cuarentena en Colombia se extiende al 1 de agosto. https://bit.ly/3231oZo

Dinero. (28 de julio de 2020e). Gobierno extiende aislamiento hasta el 30 de agosto. https://bit.ly/34LRgGd

El Espectador. (18 de agosto de 2020). En imágenes: las protestas de los comerciantes en contra de la cuarentena por localidades, en Bogotá. https://www.elespectador.com/noticias/bogota/protesta-de-comerciantes-contra-cuarentena-por-localidades-en-bogota/

El Tiempo. (28 de julio de 2020a). Cuarentena en Colombia: Duque amplía aislamiento hasta el 30 de agosto. https://bit.ly/2JsAiVl

El Tiempo. (24 de mayo de 2020b). Este es el decreto con el que se extiende el aislamiento hasta mayo 31. https://bit.ly/3kOfyoO

Ferreyra, S. G., Nieto, A. A. & Juares, W. I. (2020). Mar del Plata en Twitter: comunidades y tópicos durante la cuarentena. Enlace Universitario, 35(7), 15. https://ri.conicet.gov.ar/handle/11336/116372

Han, J., Pei, J. & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier. http://myweb.sabanciuniv.edu/rdehkharghani/files/2016/02/The-Morgan-Kaufmann-Series-in-Data-Management-Systems-Jiawei-Han-Micheline-Kamber-Jian-Pei-Data-Mining.-Concepts-and-Techniques-3rd-Edition-Morgan-Kaufmann-2011.pdf

Huang, Y., Wang, Y., Wang, H., Liu, Z., Yu, X., Yan, J., Yu, Y., Kou, C., Xu, X., Lu, J., Wang, Z., He, S., Xu, Y., He, Y., Li, T., Guo, W., Tian, H., Xu, G., Xu, X., … Wu, Y. (2019). Prevalence of mental disorders in China: a cross-sectional epidemiological study. The Lancet Psychiatry, 6(3), 211-224.  https://doi.org/10.1016/s2215-0366(18)30511-x

Jenson, H. (2020). How did “flatten the curve” become “flatten the economy?” A perspective from the United States of America. Asian Journal of Psychiatry, 51, 102165. https://doi.org/10.1016/j.ajp.2020.102165

Kearney, M. & Kearney, M. (2016). Package ‘rtweet’. CRAN. https://bit.ly/3ed6do4

Kotu, V. & Deshpande, B. (2014). Predictive analytics and data mining: concepts and practice with rapidminer. Morgan Kaufmann.

Lin, C. Y., Liaw, S. Y., Chen, C. C., Pai, M. Y. & Chen, Y. M. (2017). A computer-based approach for analyzing consumer demands in electronic word-of-mouth. Electronic Markets, 27(3), 225–242. https://doi.org/10.1007/s12525-017-0262-5

Luhn, H. (1957). A statistical approach to mechanized encoding and searching of literary information. IBM Journal of Research and Development, 1(4), 309-317. https://doi.org/10.1147/rd.14.0309

Luo, X., Estill, J., Oi, W., Meng, L., Yunlan, L., Enmei, L. & Yaolong, C. (2020). The psychological impact of quarantine on coronavirus disease 2019 (COVID-19). Psychiatry Research, 291. https://bit.ly/34MzanF

Manning, C., Raghavan, P. & Schutze, H. (2008). Text Classification and Naive Bayes. [Diapositiva de PowerPoint]. Web Stanford. https://stanford.io/2TFZM3k

Marín Correa, A. (20 de julio de 2020). Razones a favor y en contra de la cuarentena total en Bogotá. El Espectador. https://bit.ly/2HNAL3z

Martinez, E., Matin, M., Perea, J. & Ureña, A. (2011). Técnicas de clasifiación de opiniones aplicadas a un corpus en español. Procesamiento Del Lenguaje Natural, 47, 163–170.

Meier, K., Glatz, T., Guijt, M., Piccininni, M., van der Meulen, M., Atmar, K., Jolink, A.., Kurth, T., Rohmann, J. & Zamanipoor, A. (2020). Public perspectives on protective measures during the COVID-19 pandemic in the Netherlands, Germany and Italy: A survey study. PloS One, 15(8), 1-17. https://doi.org/10.1371/journal.pone.0236917

Ministerio de Salud. (6 de marzo de 2020). Colombia confirma su primer caso de COVID-19. https://www.minsalud.gov.co/Paginas/Colombia-confirma-su-primer-caso-de-COVID-19.aspx

Mohammad, S. & Bravo, F. (2017). WASSA-2017 shared task on emotion intensity. 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 34-49. https://doi.org/10.18653/v1/w17-5205

Montiel, R., García, R., Ledeneva, Y. & Cruz, R. (2009). Comparación de tres modelos de texto para la generación automática de resúmenes. Procesamiento de Lenguaje Natural, 43, 303-311. https://bit.ly/3kNIcGD

Mukherjee, R. (2020). Global efforts on vaccines for COVID-19: Since, sooner or later, we all will catch the coronavirus. Journal of Biosciences, 45(1). https://doi.org/10.1007/s12038-020-00040-7

Musinguzi, G. & Asamoah, B. (2020). The science of social distancing and total lock down: Does it work? whom does it benefit? Electronic Journal of General Medicine, 17(6), 2019-2021. https://doi.org/10.29333/ejgm/7895

Nava, A. & Grigera, J. F. (2020). Pandemia y protesta social. Jacobin Press. https://ri.conicet.gov.ar/handle/11336/116379

Nicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., Iosifidis, C., Agha, M. & Agha, R. (2020). The socio-economic implications of the coronavirus pandemic (COVID-19): A review. International Journal of Surgery (78), 185-193. https://doi.org/10.1016/j.ijsu.2020.04.018

Organización Mundial de la Salud [OMS]. (27 de abril de 2020a). Coronavirus disease (COVID-19) pandemic. https://bit.ly/3oNVLYU

Organización Mundial de la Salud [OMS]. (2020b). WHO Coronavirus Disease (COVID-19) Dashboard. https://bit.ly/3kNk1YT

Organización Internacional del Trabajo. (18 de marzo de 2020). El COVID-19 podría cobrarse casi 25 millones de empleos en el mundo. https://bit.ly/36jLPQ2

Pang, B. & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and trends in Information Retrieval, 2(1-2), 1-135. https://doi.org/10.1561/1500000011

Pratama, B., Utami, E. & Sunyoto, A. (2019). An optimization of a lexicon based sentiment analysis method on Indonesian app review. 4th International Conference on Information Technology, Information Systems and Electrical Engineering, 341-346. https://doi.org/10.1109/ICITISEE48480.2019.9003900

Peters, M. (2020). The disorder of things: Quarantine unemployment, the decline of neoliberalism, and the Covid-19 lockdown crash. Educational Philosophy and Theory, 1-4. https://doi.org/10.1080/00131857.2020.1759190

Pérez, A., Larrañaga, P. & Inza, I. (2009). Bayesian classifiers based on kernel density estimation: Flexible classifiers. International Journal of Approximate Reasoning, 50(2), 341-362. https://doi.org/10.1016/j.ijar.2008.08.008

Ramírez-Ortiz, J., Castro-Quintero, D., Lerma-Córdoba, C., Yela-Ceballos, F. & Escobar-Córdoba, F. (2020). Consecuencias de la pandemia COVID-19 en la salud mental asociadas al aislamiento social. Colombian Journal of Anesthesiology, 48(4). https://doi.org/10.5554/22562087.e930

Reddy, P., Selvaraj, S., Muralidharan, K. & Gangadhar, B. (2020). Tele-triaging: The way ahead for tertiary care psychiatry in India post-COVID-19. Indian Journal of Psychological Medicine, 42(4), 398–399. https://doi.org/10.1177/0253717620937974

Reyes-Menendez, A., Saura, J. R. & Thomas, S. B. (2020). Exploring key indicators of social identity in the #MeToo era: Using discourse analysis in UGC. International Journal of Information Management, 54, 102129.

Sáez, A. (2019). Deep learning para el reconocimiento facial de emociones básicas [Tesis de licenciatura no publicada] Universidad Politécnica de Cataluña.

Saura, J. R. (2020). Using data sciences in digital marketing: Framework, methods, and performance metrics. Journal of Innovation & Knowledge.

Saura, J. R., Palos-Sanchez, P. & Grilo, A. (2019). Detecting indicators for startup business success: Sentiment analysis using text data mining. Sustainability, 11(3), 917.

Saura, J. R., Reyes-Menendez, A. & Palos-Sanchez, P. (2019). Are black Friday deals worth it? Mining Twitter users’ sentiment and behavior response. Journal of Open Innovation: Technology, Market, and Complexity, 5(3), 58.

Sauter, D., Eisner, F., Ekman, P. & Scott, S. (2010). Cross-cultural recognition of basic emotions through nonverbal emotional vocalizations. Proceedings of the National Academy of Sciences of the United States of America, 107(6), 2408-2412. https://doi.org/10.1073/pnas.0908239106

Shimizu, K. (2020). 2019-nCoV, fake news, and racism. The Lancet, 395(10225), 685-686. https://doi.org/10.1016/S0140-6736(20)30357-3

Sigala, M. (2020). Tourism and COVID-19: Impacts and implications for advancing and resetting industry and research. Journal of Business Research, 117, 312–321. https://doi.org/10.1016/j.jbusres.2020.06.015

Steffen, B., Egli, F., Pahle, M. & Schmidt, T. (2020). Navigating the clean energy transition in the COVID-19 crisis. Joule, 4(6), 1137-1141. https://doi.org/10.1016/j.joule.2020.04.011

Teso, E., Olmedilla, M., Martínez, M. & Toral, S. (2018). Application of text mining techniques to the analysis of discourse in eWOM communications from a gender perspective. Technological Forecasting and Social Change, 129, 131-142. https://doi.org/10.1016/j.techfore.2017.12.018

Van Bavel, J., Baicker, K., Boggio, P., Capraro, V., Cichocka, A., Cikara, M., Crockett, M., Crum, A., Douglas, K., Druckman, J., Drury, J., Dube, O., Ellemers, N., Finkel, E., Fowler, J., Gelfand, M., Han, S., Haslam, A., Jetten, J., … Willer, R. (2020). Using social and behavioural science to support COVID-19 pandemic response. Nature Human Behaviour, 4(5), 460-471. https://doi.org/10.1038/s41562-020-0884-z

Weible, C., Nohrstedt, D., Cairney, P., Carter, D., Crow, D., Durnová, A., Heikkila, T., Ingold, K., McConnell, A. & Stone, D. (2020). COVID-19 and the policy sciences: initial reactions and perspectives. Policy Sciences, 53(2), 225–241. https://doi.org/10.1007/s11077-020-09381-4

Williams, C. & Kayaoglu, A. (2020). COVID-19 and undeclared work: impacts and policy responses in Europe. The Service Industries Journal, 40(13-14), 914-931. https://doi.org/10.1080/02642069.2020.1757073

Wongkar, M. & Angdresey, A. (2019). Sentiment analysis using naive bayes algorithm of the data crawler: Twitter. Fourth International Conference on Informatics and Computing (ICIC), 1-5.

Zambrano, D., Román, D. & Zambrano, M. (2019). Innovation for the analysis of feelings in text, a revision of the current technique applying crowdsourcing methodologiesEconomía y Desarrollo, 158(2), 138-146. https://bit.ly/2TPuEyh

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Universidad del Valle, Cali, Colombia.
Copyright © 2021. Fundación Universitaria Konrad Lorenz, Colombia

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