000123953 001__ 123953
000123953 005__ 20241125101127.0
000123953 0247_ $$2doi$$a10.1016/j.puhe.2022.12.003
000123953 0248_ $$2sideral$$a131719
000123953 037__ $$aART-2023-131719
000123953 041__ $$aeng
000123953 100__ $$0(orcid)0000-0002-8807-8958$$aTurón, A.$$uUniversidad de Zaragoza
000123953 245__ $$aEvolution of social mood in Spain throughout the COVID-19 vaccination process: a machine learning approach to tweets analysis
000123953 260__ $$c2023
000123953 5060_ $$aAccess copy available to the general public$$fUnrestricted
000123953 5203_ $$aObjectives: This paper presents a new approach based on the combination of machine learning techniques, in particular, sentiment analysis using lexicons, and multivariate statistical methods to assess the evolution of social mood through the COVID-19 vaccination process in Spain. Methods: Analysing 41,669 Spanish tweets posted between 27 February 2020 and 31 December 2021, different sentiments were assessed using a list of Spanish words and their associations with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy and disgust) and three valences (neutral, negative and positive). How the different subjective emotions were distributed across the tweets was determined using several descriptive statistics; a trajectory plot representing the emotional valence vs narrative time was also included. Results: The results achieved are highly illustrative of the social mood of citizens, registering the different emerging opinion clusters, gauging public states of mind via the collective valence, and detecting the prevalence of different emotions in the successive phases of the vaccination process. Conclusions: The present combination in formal models of objective and subjective information would therefore provide a more accurate vision of social reality, in this case regarding the COVID-19 vaccination process in Spain, which will enable a more effective resolution of problems.
000123953 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FEDER/S35-20R$$9info:eu-repo/grantAgreement/ES/DGA/LMP35_21
000123953 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000123953 590__ $$a3.9$$b2023
000123953 592__ $$a1.203$$b2023
000123953 591__ $$aPUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH$$b61 / 408 = 0.15$$c2023$$dQ1$$eT1
000123953 593__ $$aPublic Health, Environmental and Occupational Health$$c2023$$dQ1
000123953 591__ $$aPUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH$$b61 / 408 = 0.15$$c2023$$dQ1$$eT1
000123953 593__ $$aMedicine (miscellaneous)$$c2023$$dQ1
000123953 594__ $$a7.6$$b2023
000123953 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000123953 700__ $$0(orcid)0000-0002-0117-7655$$aAltuzarra, A.$$uUniversidad de Zaragoza
000123953 700__ $$0(orcid)0000-0002-5037-6976$$aMoreno-Jiménez, J. M.$$uUniversidad de Zaragoza
000123953 700__ $$0(orcid)0000-0001-6148-0667$$aNavarro, J.$$uUniversidad de Zaragoza
000123953 7102_ $$14014$$2623$$aUniversidad de Zaragoza$$bDpto. Economía Aplicada$$cÁrea Métodos Cuant.Econ.Empres
000123953 773__ $$g215 (2023), 83-90$$pPublic health$$tPUBLIC HEALTH$$x0033-3506
000123953 8564_ $$s1432457$$uhttps://zaguan.unizar.es/record/123953/files/texto_completo.pdf$$yVersión publicada
000123953 8564_ $$s2776579$$uhttps://zaguan.unizar.es/record/123953/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000123953 909CO $$ooai:zaguan.unizar.es:123953$$particulos$$pdriver
000123953 951__ $$a2024-11-22-11:57:51
000123953 980__ $$aARTICLE