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.

中文翻译：

---

Senti-eSystem：基于情感的eSystem，使用模糊和深度神经网络混合来衡量客户满意度

在竞争激烈的在线行业时代，了解客户的反馈和满意度是任何企业组织的重要关注之一。Twitter等著名的社交媒体平台是客户共享反馈的地方。分析客户反馈是有益的，因为它提供了一种揭示客户兴趣的有利方式。提议的系统Senti-eSystem旨在开发基于情感的eSystem使用混合的模糊和深度神经网络来衡量客户满意度，以帮助业务组织提高其服务和产品的质量。所提出的方法首先部署具有注意机制的双向长期短期记忆，以预测正负极性的情感极性，然后采用模糊逻辑方法确定客户满意度，从而进一步增强了所提出方法的功能。该系统的准确度达到92.86％，优于以前基于词典的最新方法。此外，所提出的系统的有效性也通过应用统计检验得到了验证。