Performance analysis of supervised learning models for product title classification

Abstrak

Online business development through e-commerce platforms is a phenomenon which change the world of promoting and selling products in this 21st century. Product title classification is an important task in assisting retailers and sellers to list a product in a suitable category. Product title classification is a part of text classification problem but the properties of product title are different from general document. This study aims to evaluate the performance of five different supervised learning models on data sets consist of e-commerce product titles with a very short description and they are incomplete sentences. The supervised learning models involve in the study are Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree, Support Vector Machine (SVM) and Random Forest. The results show KNN model is the best model with the highest accuracy and fastest computation time to classify the data used in the study. Hence, KNN model is a good approach in classifying e-commerce products.