Senti-eSystem: A sentiment-based eSystem-using hybridized fuzzy and deep neural network for measuring customer satisfaction

Muhammad Zubair Asghar, Fazli Subhan, Hussain Ahmad, Wazir Zada Khan, Saqib Hakak, Thippa Reddy Gadekallu, Mamoun Alazab

Research output: Contribution to journalArticle

Abstract

In the competing era of online industries, understanding customer feedback and satisfaction is one of the important concern for any business organization. The well-known social media platforms like Twitter are a place where customers share their feedbacks. Analyzing customer feedback is beneficial, as it provides an advantage way of unveiling customer interests. The proposed system, namely Senti-eSystem, aims at the development of sentiment-based eSystem using hybridized Fuzzy and Deep Neural Network for Measuring Customer Satisfaction to assist business organizations for improving the quality of their services and products. The proposed approach initially deploys a Bidirectional Long Short Term Memory with attention mechanism to predict the sentiment polarity that is positive and negative, followed by Fuzzy logic approach to determine the customer satisfaction level, which further strengthens the capabilities of the proposed approach. The system achieves an accuracy of 92.86%, outperforming the previous state-of-art lexicon-based approaches. Moreover, the effectiveness of the proposed system is also validated by applying the statistical test.

Original languageEnglish
Article numberSPE2853
Pages (from-to)1-24
Number of pages24
JournalSoftware: Practice and Experience
DOIs
Publication statusE-pub ahead of print - 3 Aug 2020

Fingerprint Dive into the research topics of 'Senti-eSystem: A sentiment-based eSystem-using hybridized fuzzy and deep neural network for measuring customer satisfaction'. Together they form a unique fingerprint.

  • Cite this