Explainable Crowd Decision Making methodology guided by expert natural language opinions based on Sentiment Analysis with Attention-based Deep Learning and Subgroup Discovery
Cristina Zuheros, Eugenio Martínez‐Cámara, Enrique Herrera‐Viedma, Iyad Katib, Francisco Herrera
Abstract
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.