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

机译：基于模糊逻辑的顾客忠诚度和决策智能系统

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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.
机译：在本文中，我们提出了一种智能方法来衡量客户对特定产品的忠诚度，并协助新客户了解产品的关键功能。我们的方法使用数据集中一组评论的综合情感评分，然后使用模糊逻辑模型来衡量客户对产品的忠诚度。我们的方法采用了一种新颖的想法，即衡量客户对产品的忠诚度，并且可以考虑新产品的各种功能和对以前客户的评论，帮助新客户对特定产品做出决定。在这项研究中，我们使用来自Amazon.com的客户在线评论的大型数据集来测试客户评论的效果。所提出的方法通过标记化，词法化和停用词去除对输入文本进行预处理，然后应用模糊逻辑方法进行决策。为了找到与某个主题的相似性和相关性，这项工作中使用了各种库和API，例如SentiWordNet，Stanford Core NLP等。所采用的方法着重于确定可能是肯定，否定和中立的评论的极性。为了找到客户的忠诚度并帮助决策，使用一组隶属函数和基于规则的模糊集系统将模糊数据分类为各种类型的忠诚度，从而应用模糊逻辑方法。该方法的实现提供了优于先前方法的94％的正确正确率的高精度的电子商务产品。

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