'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
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.Deanship of Scientific Research, The University of JordanMinisterio espanol de Economia y Competitividad
TIN2017-85727-C4-2-
