Classification of Customer Reviews Using Machine Learning Algorithms

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.

Automated summary

This article introduces a new framework for categorizing and predicting customer sentiments, and the decision tree algorithm was found to provide the best results among various machine learning algorithms used. The most important factors influencing the great customer experience were extracted using the decision tree algorithm. An interesting observation was made on the effect of the number of features on the performance of machine learning algorithms.