Mood 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

Abstract

This 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.