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Decision making models are constrained by taking the expert evaluations withpre-defined numerical or linguistic terms. We claim that the use of sentimentanalysis will allow decision making models to consider expert evaluations innatural language. Accordingly, we propose the Sentiment Analysis basedMulti-person Multi-criteria Decision Making (SA-MpMcDM) methodology for smarterdecision aid, which builds the expert evaluations from their natural languagereviews, and even from their numerical ratings if they are available. TheSA-MpMcDM methodology incorporates an end-to-end multi-task deep learning modelfor aspect based sentiment analysis, named DOC-ABSADeepL model, able toidentify the aspect categories mentioned in an expert review, and to distilltheir opinions and criteria. The individual evaluations are aggregated via theprocedure named criteria weighting through the attention of the experts. Weevaluate the methodology in a case study of restaurant choice using TripAdvisorreviews, hence we build, manually annotate, and release the TripR-2020 datasetof restaurant reviews. We analyze the SA-MpMcDM methodology in differentscenarios using and not using natural language and numerical evaluations. Theanalysis shows that the combination of both sources of information results in ahigher quality preference vector.

Sentiment Analysis based Multi-person Multi-criteria Decision Making  Methodology using Natural Language Processing and Deep Learning for Smarter  Decision Aid. Case study of restaurant choice using TripAdvisor reviews