Measuring Customer Satisfaction Using a Fuzzy Inference System

Darestani, A. Y.; Jahromi, Abdolhamid Eshraghniaye

doi:10.3923/jas.2009.469.478

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…In this step Fuzzy rules are designed which are simple and computed as follows, 40 where if part depicts the antecedent, and then is known as the consequent.

IF (antecedent) THEN (consequence)

…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2020

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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.

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“…In this step Fuzzy rules are designed which are simple and computed as follows, 40 where if part depicts the antecedent, and then is known as the consequent.

IF (antecedent) THEN (consequence)

…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Kwong and Bai (2002) used fuzzy numbers and fuzzy scales to determine the importance weights of customer requirements in the context of quality function deployment, improving the imprecise ranking of customer requirements. Darestani and Jahromi (2009) proposed a new customer satisfaction index based on FS rules aimed to translate in mathematical terms linguistic statements expressed by experienced people. Castillo and Lorenzana (2010), through the linguistic approach of the FS theory, modelled the opinions of experts combining both quantitative and qualitative performance measures.…”

Section: Application Of Fuzzy Theory: An Overviewmentioning

confidence: 99%

Intuitionistic fuzzy sets in questionnaire analysis

Quatto

Ripamonti

2015

Qual Quant

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Fuzzy sets represent an extension of the concept of set, used to mathematically model veiled and indefinite concepts, such as those of youth, poverty, customer satisfaction and so on. Fuzzy theory introduces a membership function, expressing the degree of membership of the elements to a set. Intuitionistic fuzzy sets and hesitant fuzzy sets are two extensions of the theory of fuzzy sets, in which non-membership degrees and hesitations expressed by a set of experts are, respectively, introduced. In this paper, we apply intuitionistic fuzzy sets to questionnaire analysis, with a focus on the construction of membership, non-membership and uncertainty functions. We also suggest the possibility of considering intuitionistic hesitant fuzzy sets as a valuable theoretical framework. We apply these models to the evaluation of a Public Administration and we assess our results through a sensitivity analysis.

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“…In this step, the fuzzy rules are designed to take the conclusion. These rules are simple and expressed as follow [31]:…”

Section: Design If/then Rulesmentioning

confidence: 99%

Fuzzy-Based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

Asghar¹,

Ullah²,

Shamshirband³

et al. 2019

Preprint

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The feedback collection and analysis has remained an important subject matter since long. The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis. However, the student expresses their feedback opinions on online social media sites, which need to be analyzed. This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews. Our technique computes the sentiment score of student feedback reviews and then applies fuzzy-logic module to analyze and quantify student’s satisfaction at the fine-grained level. The experimental results reveal that the proposed work has outperformed the baseline studies as well as state-of-the-art machine learning classifiers.

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Measuring Customer Satisfaction Using a Fuzzy Inference System

Cited by 10 publications

References 12 publications

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

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

Intuitionistic fuzzy sets in questionnaire analysis

Fuzzy-Based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

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