Explainable Crowd Decision Making methodology guided by expert natural language opinions based on Sentiment Analysis with Attention-based Deep Learning and Subgroup Discovery
Metadatos
Afficher la notice complèteAuteur
Zuheros, Cristina; Martínez Cámara, Eugenio; Herrera Viedma, Enrique; Herrera Triguero, FranciscoEditorial
Elsevier
Materia
Crowd decision making Explainability Attention mechanisms Subgroup discovery
Date
2023-04-27Referencia bibliográfica
C. Zuheros et al. Explainable Crowd Decision Making methodology guided by expert natural language opinions based on Sentiment Analysis with Attention-based Deep Learning and Subgroup Discovery. Information Fusion 97 (2023) 101821[https://doi.org/10.1016/j.inffus.2023.101821]
Patrocinador
PID2020-119478GBI00,; PID2019-103880RB-I00; PID2020-116118GA-I00; MCIN/AEI/10.13039/501100011033; ERDF A way of making Europe; PRE2018-083884 funded by MCIN/AEI/10.13039/501100011033; ESF Investing in your future; Universidad de Granada / CBUARésumé
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