Crowd Decision Making: Sparse Representation Guided by Sentiment Analysis for Leveraging the Wisdom of the Crowd

Abstract

The 'wisdom of the crowd' theory states that a nonexpert crowd makes smarter decisions than a reduced set of experts. Social network platforms are a source of evaluations in the natural language of any topic, which may be considered as the evaluations of a nonexpert crowd. Decision-making (DM) models are constrained by their inability of processing large amounts of evaluations in natural language, as those ones from social networks. We claim that evaluations from social networks can enhance the quality of multiperson multicriteria DM models. Accordingly, we propose a crowd DM model guided by sentiment analysis (SA), which solves decision situations leveraging the wisdom of the crowd available in social networks. The model uses several deep-learning SA classification models through opinion triplets to incorporate all the evaluation shades in the DM model. Likewise, the likely lack of information stemmed from the consideration of a large set of users is tackled with a sparse representation of the evaluations. We annotate and release the TripR-2020Large dataset, and we use it to evaluate the model in the use case of restaurant choice. The results show that the integration of the wisdom of the crowd and the different shades of the evaluations enhances the quality of the decision.