Utilizing Topic Modelling in Customer Product Review for Classifying Baby Product

Abstract

E-commerce is growing at a breakneck pace. As a result, online shopping has increased, which has increased online product reviews. Often, we come across Amazon products with thousands of reviews, and if we look closely we discover that some of them are completely unrelated to the product. In this study, we conducted research on how product review classification can assist in resolving the issue of comments on incorrect items. The method used in this research consists of 4 steps which are, data acquisition, data pre-processing, topic modeling, and text classification. Where Latent Dirichlet Allocation (LDA) was used as our topic modeling technique, and for text classification we used Support Vector Machine (SVM), Logistic Regression, and Multi-Layer Perceptron (MLP) classifiers. We found out that by combining both topic modeling and text classification, a powerful tool for handling this kind of problem was developed. Adding the topic modeling can improve the model's accuracy performance from 0.61 to 0.78. So, we can conclude that the topic modeling was useful in classifying the product reviews.