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

Abstract

Summary 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.

Decades on emergency decision-making: a bibliometric analysis and literature review

“… Another possible direction for future work would be to apply artificial intelligence techniques to efficiently improve the capability and enhance the features of EDM. For example, deep learning-based optimization models [ 1 , 20 ] can be used to calculate the weight vector of criteria weights objectively based on the assessment information of emergency schemes. Also, EDM methods can be empowered by neural networks to consider fluctuations in the determination of a desirable alternative when responding to an emergency event.…”

“…Figure 9 depicts the time line visualization of co-occurrence keywords network and associated clustering analysis result. According to this figure, the following results can be obtained: (1) The research period on EDM improvement can be divided into nascency stage, growth stage, explosion is the nascency stage, in which rare co-occurrence keywords emerged. Between 2012 and 2014, more researchers focused on the research about "air pollution", "industrial safety and security management", "case-based reasoning", "cumulative prospect theory", "fault tree analysis" and "decision support system", and the studies of this field began to grow progressively.…”

“…A total of 303 articles published between 2010 and 2020 were collected from the Web of Science database. Based on the selected articles related to EDM, main contributions of the current work are listed as follows: (1) The research trends according to the number of articles on EDM are identified. (2) The most productive countries, institutions, and authors, and their cooperation relationships are elucidated by the cooperation network analysis.…”

TagNN: A Code Tag Generation Technology for Resource Retrieval from Open‐Source Big Data

“…TagNN involves two main methods. One is a tag generation model based on deep learning methods, and the other uses TF-IDF to extract keywords from generated code snippets [21][22][23][24]. The deep learning model has a good effect on natural language processing, and TF-IDF has a good effect on extracting key information in the text.…”

An integrated three-tier trust management framework in mobile edge computing using fuzzy logic

“…This motivates them to enhance their processing performance capabilities and limits any malicious actions to happen. The trust evaluation scheme allows a service provider to know its faulty points to improve them, ( Asghar et al, 2020 ). Service providers with good trust value will attract more service users, which increases their profits.…”