ABSTRACT

The information resulting from the use of the organization's products and services is a valuable resource for business analytics. Therefore, it is necessary to have systems to analyze customer reviews. This article is about categorizing and predicting customer sentiments. In this article, a new framework for categorizing and predicting customer sentiments was proposed. The customer reviews were collected from an international hotel. In the next step, the customer reviews processed, and then entered into various machine learning algorithms. The algorithms used in this paper were support vector machine (SVM), artificial neural network (ANN), naive bayes (NB), decision tree (DT), C4.5 and k-nearest neighbor (K-NN). Among these algorithms, the DT provided better results. In addition, the most important factors influencing the great customer experience were extracted with the help of the DT. Finally, very interesting results were observed in terms of the effect of the number of features on the performance of machine learning algorithms.

摘要

使用组织的产品和服务所产生的信息是用于业务分析的宝贵资源。因此，有必要建立分析顾客评论的系统。本文是关于对客户情绪进行分类和预测。在本文中，提出了一种用于分类和预测客户情绪的新框架。客户评论来自一家国际酒店。下一步，处理客户评论，然后输入各种机器学习算法。本文使用的算法是支持向量机（SVM），人工神经网络（ANN），朴素贝叶斯（NB），决策树（DT），C4.5和k最近邻（K-NN）。在这些算法中，DT提供了更好的结果。此外，在DT的帮助下，提取了影响优质客户体验的最重要因素。最后，就功能数量对机器学习算法性能的影响而言，观察到了非常有趣的结果。