A Fuzzy Logic Based Intelligent System for Measuring Customer Loyalty and Decision Making

Abstract

In this paper, an intelligent approach is presented to measure customers’ loyalty to a specific product and assist new customers regarding a product’s key features. Our approach uses an aggregated sentiment score of a set of reviews in a dataset and then uses a fuzzy logic model to measure customer’s loyalty to a product. Our approach uses a novel idea of measuring customer’s loyalty to a product and can assist a new customer to take a decision about a particular product considering its various features and reviews of previous customers. In this study, we use a large sized data set of online reviews of customers from Amazon.com to test the performance of the customer’s reviews. The proposed approach pre-processes the input text via tokenization, Lemmatization and removal of stop words and then applies fuzzy logic approach to take decisions. To find similarity and relevance to a topic, various libraries and API are used in this work such as SentiWordNet, Stanford Core NLP, etc. The approach utilized focuses on identifying polarity of the reviews that may be positive, negative and neutral. To find customer’s loyalty and help in decision making, the fuzzy logic approach is applied using a set of membership functions and rule-based system of fuzzy sets that classify data in various types of loyalty. The implementation of the approach provides high accuracy of 94% of correct loyalty to the e-commerce products that outperforms the previous approaches.

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

“…We investigate Fuzzy-based sentiment analysis for analyzing student feedback and satisfaction inspired by Ghani et al [4] work on applying fuzzy-based SA for measuring customer loyalty, we leverage fuzzy-based SA for analyzing student feedback and satisfaction.…”

“…In this technique scores of user sentiments are computed using different dictionaries of sentiment words annotated with their semantic orientation i.e. polarity and strength [1] , and in next phase, fuzzy logic module can be applied to compute user satisfaction at different levels of granularities [4]. In this work, we present a novel technique of utilizing aggregated sentiment score of each student's feedback (review) and make it input to the fuzzy logic module for quantifying student feedback and satisfaction.…”

A Fuzzy Logic Algorithm for Optimizing the Investment Decisions within Companies

“…Fuzzy logic was used by Zhang [29] to assess commercial viability of technology start-up businesses in a government venture capital, while Shen and Tzeng [30] developed a fuzzy inferenceenhanced VC-DRSA model for technical analysis of the investment. Other researches have been conducted in the area of: knowledge management performance measurement of small and medium enterprises [31], social behavior modeling [32], measuring customer loyalty [33], e-commerce [34], MADM (multi-attribute decision-making) [35][36][37][38][39][40][41], material selection [42], risk assessment [43], medical diagnosis [44], mobile robots [45], investment selection [46] and forecasting [47].…”

The Development of a Fuzzy Logic System in a Stochastic Environment with Normal Distribution Variables for Cash Flow Deficit Detection in Corporate Loan Policy

“…Aksoy et al [11] constructed a fuzzy logic system to solve multi-period dynamic decision making for strategic supplier selection with stochastic demand, while Ghani et al [12] developed a fuzzy logic intelligent system for measuring customer loyalty and decision making. Bolos et al [13] elaborated on a model that adjusts the GM(1, 2) errors for financial data series that measure changes in the public sector financial indicators.…”