Sentiment Analysis for e-Payment Service Providers Using Evolutionary eXtreme Gradient Boosting
Metadatos
Mostrar el registro completo del ítemEditorial
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Materia
Evolutionary Genetic algorithms Neutrality Detector Model Sentiment analysis Social networks XGBoost
Fecha
2020Referencia bibliográfica
D. A. Al-Qudah, A. M. Al-Zoubi, P. A. Castillo-Valdivieso and H. Faris, "Sentiment Analysis for e-Payment Service Providers Using Evolutionary eXtreme Gradient Boosting," in IEEE Access, vol. 8, pp. 189930-189944, 2020, doi: 10.1109/ACCESS.2020.3032216.
Patrocinador
Deanship of Scientific Research, The University of Jordan; Ministerio espanol de Economia y Competitividad TIN2017-85727-C4-2-PResumen
Online services depend primarily on customer feedback and communications. When this
kind of input is lacking, the overall approach of the service provider can shift in unintended ways. These
services rely on feedback to maintain consumer satisfaction. Online social networks are a rich source
of consumer data related to services and products. Well developed methods like sentiment analysis can
offer insightful analyses and aid service providers in predicting outcomes based on their reviews—which,
in turn, enables decision-makers to develop effective strategic plans. However, gathering this data is
more challenging on Arabic online social networks, due to the complexity of the Arabic language and
its dialects. In this study, we propose an approach to sentiment analysis that combines a neutrality
detector model with eXtreme Gradient Boosting and a genetic algorithm to effectively predict and analyze
customers’ opinions of an e-Payment service through an Arabic social network. The proposed approach
yields excellent results compared to other approaches. Feature analysis is also conducted on consumer
reviews to identify influencing keywords.