000150564 001__ 150564
000150564 005__ 20250210102457.0
000150564 0247_ $$2doi$$a10.1111/itor.13568
000150564 0248_ $$2sideral$$a142631
000150564 037__ $$aART-2024-142631
000150564 041__ $$aeng
000150564 100__ $$0(orcid)0000-0003-3138-7597$$aAguarón, J.$$uUniversidad de Zaragoza
000150564 245__ $$aMood and emotion assessment for risk reduction of pandemic spread through passenger air transport: a DSS applied to the COVID-19 in the case of Spain
000150564 260__ $$c2024
000150564 5060_ $$aAccess copy available to the general public$$fUnrestricted
000150564 5203_ $$aThis paper presents a decision support system (DSS) for sentiment analysis of Spanish texts based on lexicons. The information provided by this DSS, named Spanish Sentiment Analysis‐DSS (SSA‐DSS), is employed to assess the social impacts considered in an external software module (RRPS‐PAT) centered on risk reduction of pandemic spread through passenger air transport. RRPS‐PAT is a complex multiobjective optimization module simultaneously addressing different conflicting objectives, including epidemiological, economic, and social aspects. This allows more effective and realistic decisions to be made. The specificity and novelty of the problem suggest the use of lexicon‐based approaches because there is no prior information about the problem to train machine learning–based approaches. The SSA‐DSS covers the entire process from the incorporation of texts, particularly tweets, to be analyzed, the application of preprocessing and cleaning tools, the selection of lexicons (general, context, and emoji lexicons) to be used and their possible modification, to the visualization of results and their exportation to other software tools. This paper contemplates, apart from the RRPS‐PAT module, the connection with a social network analysis tool (Gephi) that complements the information provided by SSA‐DSS with the identification of social leaders. The usefulness and functionalities of SSA‐DSS are illustrated by means of an example related to the evolution of societal mood in Spain during the COVID‐19 pandemic.
000150564 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2021-122209OB-C31$$9info:eu-repo/grantAgreement/ES/AEI/RED2022-134540-T$$9info:eu-repo/grantAgreement/ES/DGA/LMP 35-21$$9info:eu-repo/grantAgreement/ES/MICINN/PID2022-139863OB-I00
000150564 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000150564 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000150564 700__ $$0(orcid)0000-0002-0117-7655$$aAltuzarra, A.$$uUniversidad de Zaragoza
000150564 700__ $$aAznar, R.
000150564 700__ $$0(orcid)0000-0003-4419-1905$$aEscobar, M.T.$$uUniversidad de Zaragoza
000150564 700__ $$aJiménez-Martín, A.
000150564 700__ $$aMateos, A.
000150564 700__ $$aMoreno-Díaz, A.
000150564 700__ $$0(orcid)0000-0002-5037-6976$$aMoreno-Jiménez, J.M.$$uUniversidad de Zaragoza
000150564 700__ $$aMoreno-Loscertales, C.$$uUniversidad de Zaragoza
000150564 700__ $$0(orcid)0000-0002-2405-4375$$aMuerza, V.$$uUniversidad de Zaragoza
000150564 700__ $$0(orcid)0000-0001-6148-0667$$aNavarro, J.$$uUniversidad de Zaragoza
000150564 700__ $$aSarango, A.
000150564 700__ $$0(orcid)0000-0002-8807-8958$$aTurón, A.$$uUniversidad de Zaragoza
000150564 700__ $$aVargas, L.G.
000150564 7102_ $$11007$$2610$$aUniversidad de Zaragoza$$bDpto. Medicina, Psiqu. y Derm.$$cArea Medicina
000150564 7102_ $$14014$$2623$$aUniversidad de Zaragoza$$bDpto. Economía Aplicada$$cÁrea Métodos Cuant.Econ.Empres
000150564 773__ $$g(2024), 1-32$$pInt. trans. oper. res.$$tINTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH$$x0969-6016
000150564 8564_ $$s4075425$$uhttps://zaguan.unizar.es/record/150564/files/texto_completo.pdf$$yVersión publicada
000150564 8564_ $$s2519473$$uhttps://zaguan.unizar.es/record/150564/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000150564 909CO $$ooai:zaguan.unizar.es:150564$$particulos$$pdriver
000150564 951__ $$a2025-02-10-08:28:21
000150564 980__ $$aARTICLE