A Fuzzy Logic Based Intelligent System for Measuring Customer Loyalty and Decision Making
A Fuzzy Logic Based Intelligent System for Measuring Customer Loyalty and Decision Making
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DOI：
10.3390/sym10120761
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发表时间：
2018-12-01
期刊：
影响因子：
2.7
通讯作者：
Ashfaq, Aimen
中科院分区：
文献类型：
--
作者：
Ghani, Usman;Bajwa, Imran Sarwar;Ashfaq, Aimen
在本文中，提出了一种智能方法来衡量客户对特定产品的忠诚度，并帮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汇总情感得分，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衡量客户对产品忠诚度的新理念，可以帮助新客户考虑到产品的各种特性和以前客户的评论，对特定产品做出决定。在这项研究中，我们使用了一个来自亚马逊网站的大型在线客户评论数据集来测试客户评论的性能。该方法对输入文本进行标记化、词法化和去除停止词的预处理，然后应用模糊逻辑方法进行决策。为了找到与主题的相似性和相关性，在这项工作中使用了各种库和API，如SentiWordNet， Stanford Core NLP等。所使用的方法侧重于识别评论的极性，可能是积极的，消极的和中立的。为了发现顾客的忠诚度并帮助决策，模糊逻辑方法被应用于一组隶属函数和基于规则的模糊集系统，这些模糊集系统对不同类型的忠诚度数据进行分类。该方法的实施为电子商务产品提供了高达94%的正确忠诚度，优于以往的方法。
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.