This work aims to generate classification models that help determine the colour of an epidemiological semaphore (ES) by analysing online news and being better prepared for the different changes in the evolution of the pandemic. To accomplish this, we introduce Cov-NES-Mex corpus, a collection of 77,983 news (labelled with the Mexican ES system) related to Covid-19 for the 32 regions of Mexico. Also, we showed measures that describe the corpus as imbalanced and with a high vocabulary overlap between classes. In addition, evaluation measurements of the pandemic by region are proposed. Furthermore, a classification model, based on a transformer architecture specialised for the Spanish language, achieved up to 0.83 of F-measure. Thus, this work provides evidence that there is essential information in the news that can be used to determine the colour of the ES up to 4 weeks in advance. Finally, the presented results could be applied to other Spanish-speaking countries, which do not have an ES system, thus inferring and comparing their situation concerning the Mexican ES.
Cervantes HolguínEGutiérrez SandovalPR. Resistir la covid-19. Intersecciones en la educación de ciudad juárez, méxico. Revista Internacional Educación para Justicia Social2020; 9(3): 7–23.
2.
Guerrero-RodriguezRÁlvarez-CarmonaMÁArandaRet al. Studying online travel reviews related to tourist attractions using NLP methods: the case of Guanajuato, Mexico. Curr Issues Tour. Epub ahead of print 26November2021. DOI: 10.1080/13683500.2021.2007227.
3.
CrickJMCrickD. Coopetition and COVID-19: collaborative business-to-business marketing strategies in a pandemic crisis. Ind Market Manag2020; 88: 206–213.
4.
Arce-CardenasSFajardo-DelgadoDÁlvarez-CarmonaMÁet al. A tourist recommendation system: a study case in Mexico. In: Mexican international conference on artificial intelligence. Springer, pp. 184–195, https://link.springer.com/chapter/10.1007/978-3-030-89820-5_15
5.
Salinas ZuñigaJI. Estudio descriptivo de la aplicación del Semáforo Epidemiológico y su efecto en el comercio de la Ciudad de Babahoyo. BS Thesis, UTB, Babahoyo, 2020.
6.
KowsariKJafari MeimandiKHeidarysafaMet al. Text classification algorithms: a survey. Information2019; 10(4): 150.
7.
BarberáPBoydstunAELinnSet al. Automated text classification of news articles: a practical guide. Polit Anal2021; 29(1): 19–42.
8.
Ortega-RubioAMurillo-AmadorBDíaz-CastroSet al. Covid-19: Los reajustes en el trabajo de investigación científica en méxico. Terra Latinoamericana2020; 38(4): 917–930.
9.
PadurariuCBreabanME. Dealing with data imbalance in text classification. Procedia Comput Sci2019; 159: 736–745.
WickePBolognesiMM. Framing COVID-19: how we conceptualize and discuss the pandemic on Twitter. PLoS ONE2020; 15(9): e0240010
13.
ChenLCChangKH. A novel corpus-based computing method for handling critical word-ranking issues: an example of COVID-19 research articles. Int J Intell Syst2021; 36(7): 3190–3216.
14.
PraveenSIttamallaRDeepakG. Analyzing the attitude of Indian citizens towards COVID-19 vaccine – a text analytics study. Diabetes Metab Syndr: Clin Res Rev2021; 15(2): 595–599.
15.
AndersonBS. Using text mining to glean insights from COVID-19 literature. J Inf Sci. Epub ahead of print 16March2021. DOI: 10.1177/01655515211001661.
16.
GonzaloJMontes-y-GómezMRossoP. IberLEF 2021 Overview: natural language processing for Iberian languages. In: Proceedings of the Iberian languages evaluation forum (IberLEF 2021) co-located with the conference of the Spanish Society for Natural Language Processing (SEPLN 2021), XXXVII international conference of the Spanish Society for Natural Language Processing, Málaga, vol. 2943, pp. 1–15, http://ceur-ws.org/Vol-2943/Overview_iberLEF_2021.pdf
17.
MaggeAKleinAMiranda-EscaladaAet al. Overview of the sixth social media mining for health applications (#SMM4H) shared tasks at NAACL 2021. In: Proceedings of the sixth social media mining for health (#SMM4H) workshop and shared task. Mexico City, Mexico: Association for Computational Linguistics, pp. 21–32, https://aclanthology.org/2021.smm4h-1.4
18.
KleinAZMaggeAO’ConnorKet al. Toward using Twitter for tracking COVID-19: a natural language processing pipeline and exploratory data set. J Med Internet Res2021; 23(1): e25314.
19.
BandaJMTekumallaRWangGet al. A large-scale COVID-19 Twitter chatter dataset for open scientific research – an international collaboration. Epidemiologia2021; 2(3): 315–324.
20.
AgerriRCentenoREspinosaMet al. VaxxStance@IberLEF 2021: overview of the task on going beyond text in cross-lingual stance detection. Procesamiento del Lenguaje Natural2021; 67: 173–181.
21.
VaishyaRJavaidMKhanIHet al. Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr: Clin Res Rev2020; 14(4): 337–339.
CuryRCMegyeriILindseyTet al. Natural language processing and machine learning for detection of respiratory illness by chest CT imaging and tracking of COVID-19 pandemic in the US. Radiol: Cardiothoracic Imag2021; 3(1): e200596.
24.
LeeJKimKParkGet al. The role of online news and social media in preventive action in times of infodemic from a social capital perspective: the case of the COVID-19 pandemic in South Korea. Telemat Inform2021; 64: 101691.
25.
CaldarelliGDe NicolaRPetrocchiMet al. Flow of online misinformation during the peak of the COVID-19 pandemic in Italy. EPJ Data Sci2021; 10(1): 34.
26.
ApukeODOmarB. Fake news and COVID-19: modelling the predictors of fake news sharing among social media users. Telemat Inform2021; 56: 101475.
27.
KimJAumJLeeSet al. FibVID: comprehensive fake news diffusion dataset during the covid-19 period. Telemat Inform2021; 64: 101688.
28.
ChenLCChangKHChungHY. A novel statistic-based corpus machine processing approach to refine a big textual data: an ESP case of COVID-19 news reports. Appl Sci2020; 10(16): 5505.
SalisuAAVoXV. Predicting stock returns in the presence of COVID-19 pandemic: the role of health news. Int Rev Financ Anal2020; 71: 101546.
31.
BunevicieneIBuneviciusRBagdonasSet al. COVID-19 media fatigue: predictors of decreasing interest and avoidance of COVID-19–related news. Public Health2021; 196: 124–128.
32.
BaschCHHillyerGCMeleo-ErwinZet al. News coverage of the covid-19 pandemic: missed opportunities to promote health sustaining behaviors. Infect Dis Health2020; 25(3): 205–209.
33.
Escolà-GascónÁ. New techniques to measure lie detection using COVID-19 fake news and the Multivariable Multiaxial Suggestibility Inventory-2 (MMSI-2). Comput Human Behav Rep2021; 3: 100049.
34.
Gómez-AdornoHPosadas-DuránJPEnguixGBet al. Overview of FakeDeS at IberLEF 2021: fake news detection in Spanish shared task. Procesamiento del Lenguaje Natural2021; 67: 223–231.
35.
PintoDRossoPJiménez-SalazarH. On the assessment of text corpora. In: HoracekHMétaisEMuñozRet al. (eds) Natural language processing and information systems. Berlin, Heidelberg: Springer, pp. 281–290.
36.
PintoDRossoPJiménez-SalazarH. A self-enriching methodology for clustering narrow domain short texts. Comput J2011; 54(7): 1148–1165.
37.
HernándezLLópez-LópezAMedinaJE. Classification of attitude words for opinion mining. Int J Comput Linguist Appl2011; 2(1–2): 267–283.
38.
AnithaPPatilMM. RFM model for customer purchase behavior using K-means algorithm. J King Saud Univ – Comput Inform Sci2022; 34: 1785–1792.
39.
SyakurMAKhotimahBKRochmanEMSet al. Integration K-means clustering method and elbow method for identification of the best customer profile cluster. IOP Conf Ser: Mater Sci Eng2018; 336: 012017.
40.
RodríguezJMCalotEMerlinoH. Clasificación de prescripciones médicas en español. In: XX Congreso Argentino de Ciencias de la Computación (Buenos Aires, 2014), Buenos Aires, Argentina, p. 10, http://sedici.unlp.edu.ar/handle/10915/42402
41.
Alvarez-CarmonaMÁArandaR. Determinación, automática del color del semáforo mexicano del COVID-19 a partir de las noticias. Research Comput Sci2022. DOI: 10.1590/SciELOPreprints.3834.
42.
IwendiCPonnanSMunirathinamRet al. An efficient and unique TF/IDF algorithmic model-based data analysis for handling applications with big data streaming. Electronics2019; 8(11): 1331.
43.
ThabtahFHammoudSKamalovFet al. Data imbalance in classification: experimental evaluation. Inform Sci2020; 513: 429–441.
44.
UmadeviSMarselineKJ. A survey on data mining classification algorithms. In: 2017 international conference on signal processing and communication (ICSPC), Coimbatore, India, 28–29July2017, pp. 264–268. New York: IEEE.
45.
DevlinJChangMWLeeKet al. BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
46.
VásquezJGómez-AdornoHBel-EnguixG. Bert-based approach for sentiment analysis of Spanish reviews from tripadvisor. In: Proceedings of the third workshop for Iberian languages evaluation forum (IberLEF 2021), CEUR WS proceedings, http://ceur-ws.org/Vol-2943/restmex_paper6.pdf
47.
Álvarez-CarmonaMÁArandaRArce-CardenasSet al. Overview of Rest-Mex at IberLEF 2021: recommendation system for text Mexican tourism. Procesamiento del Lenguaje Natural2021; 67: 163–172.
48.
AmigóEGonzaloJMizzaroSet al. An effectiveness metric for ordinal classification: formal properties and experimental results. arXiv preprint arXiv:2006.01245, 2020.
49.
LiuHZhouMLuXSet al. Weighted Gini index feature selection method for imbalanced data. In: 2018 IEEE 15th international conference on networking, sensing and control (ICNSC), Zhuhai, China, 27–29 March 2018, pp. 1–6. New York: IEEE.
