Design of an Efficient Integrated Feature Engineering based Deep Learning Model Using CNN for Customer's Review Helpfulness Prediction

Online customer feedback is essential for promoting online buying. The e-commerce sector has experienced exponential growth since COVID-19. Now days the growth of any business in e-commerce industry is highly influenced by the online consumer reviews and a lot of research has been conducted by numerous researchers in this regard to determine the reviews helpfulness for experience and search based products. In the study, the author's aims to develop a convolutional neural network based binary classification model for assessing the usefulness of the products reviews through the analysis of consumer evaluations with reference to products and services in e-commerce. In this experiment, predicting helpfulness is considered as binary research problem in order to determine review helpfulness in association with the structural, emotional, linguistic, emotive, lexical, and voting feature sets. In this study, various machine learning algorithms viz., K-nearest neighbor (KNN), Linear regression (LR), Gaussian naive bays (GNB), linear discriminant analysis (LDA), support vector machine (SVM) and convolution neural networks (CNN) were used to build the classification models and their results were compared with each other and other existing state of art models. From the simulation results, it was observed that CNN outperformed over the above stated algorithms and other existing state-of-the-art classification models, achieving 99.72% and 99.97% accuracy for two different search and experience based datasets. Furthermore, the performance of these models were also evaluated in terms of precision, recall, and F1 score. The findings presented in this paper reveals the importance of machine learning models in selecting the quality products.