Phân tích ý kiến khách hàng trong thương mại điện tử – Tiếp cận theo phương pháp học máy kết hợp kiểm định Bootstrap - Hồ Trung , Thành; Trần Thị , Ánh; Huỳnh Thanh , Tuyền (JOURNAL OF ASIAN BUSINESS AND ECONOMIC STUDIES, 2021)
The development of the Web 2.0 generation has created an opportunity for interaction between customers and businesses through e-commerce channels more effectively. Customers can post feedbacks by leaving textual comments that are the natural language in Vietnamese related to products or services which they have experienced. As a result, businesses can manage and analyze opinions to deeply understand the customers' experiences to attract and retain their customers. This is an important and effective approach for businesses to create a competitive advantage. In this article, the authors concentrate on proposing a method of analyzing customers' opinions based on the natural language processing method which is combined with Self-Organizing Map (SOM) and K-Means. In addition, the T-test technique with the Bootstrap method is applied to evaluate the results in order to select the appropriate clustering method for the case of a dataset that is collected from customers' feedbacks of Tiki's e-commerce site. The proposed method has high accuracy and effective applicability to customer experience analysis.
Keywords: SOM; K-Means; Bootstrap method; Natural language processing; Online customer reviews; E-commerce