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Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language. Accordingly, we propose the Sentiment Analysis based Multi-person Multi-criteria Decision Making (SA-MpMcDM) methodology for smarter decision aid, which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available. The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DOC-ABSADeepL model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria. The individual evaluations are aggregated via the procedure named criteria weighting through the attention of the experts. We evaluate the methodology in a case study of restaurant choice using TripAdvisor reviews, hence we build, manually annotate, and release the TripR-2020 dataset of restaurant reviews. We analyze the SA-MpMcDM methodology in different scenarios using and not using natural language and numerical evaluations. The analysis shows that the combination of both sources of information results in a higher quality preference vector. • Decision making models are limited by pre-defined numerical and linguistic terms. • We propose a methodology to deal with natural language and numerical assessments. • We design a deep learning model for distilling opinions from written assessments. • We present and release a dataset, which can be used for evaluating MpMcDM models. [ABSTRACT FROM AUTHOR]

There exist a high demand to provide explainability to artificial intelligence systems, where decision making models are included. This paper focuses on crowd decision making using natural language evaluations from social media with the aim to provide explainability. We present the Explainable Crowd Decision Making based on Subgroup Discovery and Attention Mechanisms (ECDM-SDAM) methodology as an a posteriori explainable process that captures the wisdom of crowds that is naturally provided in social media opinions. It extracts the opinions from social media texts using a deep learning based sentiment analysis approach called Attention based Sentiment Analysis Method. The methodology includes a backward process that provides explanations to justify its sense-making procedure by applying mainly the attention mechanism on texts and subgroup discovery on opinions. We evaluate the methodology in the real case study of the TripR-2020Large dataset for restaurant choice. The results show that the ECDM-SDAM methodology provides easy understandable explanations that elucidates the key reasons that support the output of the decision process. • Explainability in decision making is essential to increase its use and understanding. • Attention mechanisms and subgroup discovery can generate explainable decision making. • We propose a methodology that offers explanations of its internal decision mechanism. • The proposed methodology captures the wisdom of crowds from social media. • Natural language with sentiment analysis and deep learning enriches expert evaluation. [ABSTRACT FROM AUTHOR]

Abstract A particular challenge in Natural Language Processing is the disambiguation of polysemic words. The great availability, diversity and the speed of changing of the data from on-line sources force the development of disambiguation systems with a reduced dependency on linguistic resources. We argue that the contextual neural encoding of a specific entity avoids the need of using external linguistic resources like knowledge bases. Hence, we propose a neural network architecture grounded in the use of Long Short-Term Memory Recurrent Neural Network for encoding the context of a target geographical entity, specifically Two k-Contextual Windows model for the disambiguation of the geographical entity Granada. We generate two annotated corpora of texts from social media written in English and Spanish, which we use to evaluate our proposal. The results show that our claim holds. Highlights • A challenge in Natural Language Processing is the disambiguation of polysemic words. • Named entities are also polysemic, so their disambiguation is required. • Disambiguation methods highly depend on linguistic resources. • We propose a deep recurrent neural network that does not use any linguistic resourse. • The results show that our model can disambiguate the target entity. [ABSTRACT FROM AUTHOR]