An intelligent DMI-based feature selection approach for measuring customer loyalty using SVM

The business competition among different companies has exponentially increased in recent years. To remain in business, there is a pressing priority for an increased focus on customer satisfaction that ultimately fosters customer loyalty. The customer loyalty analysis is critically important to retaining current customers and attracting more new customers. The proposed study focuses on an efficient approach to determining the customer’s loyalty and satisfaction with a product. This is determined by using machine intelligence and sentiment analysis of a large dataset of product reviews which is obtained through Amazon. The novel Feature Selection Method is performed to improve performance for large data sets. This feature selection method is based on Dynamic Mutual Information (DMI) which helps in selecting only important features to reduce the dimensionality problems of very large datasets. Text preprocessing is performed initially, which includes Stopword removal, token, and lemma creation. SentiWordNet along with the Intelligent SVM technique is implemented for aspect-level sentiment analysis to categorize customer reviews into three different classes of loyalty.