Integrated feature engineering based deep learning model for predicting customer’s review helpfulness

Article type: Research Article

Authors: Sharma, Surya Prakash^{a; 1} | Singh, Laxman^{b; *} | Tiwari, Rajdev^c

Affiliations: [a] Research Scholar, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, U.P., India | [b] Department of Electronics & Communication Engineering, Noida Institute of Engineering, & Technology, Greater Noida, U.P., India | [c] Depty General Manager, CEEKH Edunix India Pvt. Ltd. Noida, U.P., India

Correspondence: [*] Corresponding author. Laxman Singh, Department of Electronics & Communication Engineering, Noida Institute of Engineering, & Technology, Greater Noida, U.P., India. E-mail: [email protected].

Note: [1] Department of Computer Science & Engineering, Noida Institute of Engineering, & Technology, Greater Noida, U.P., India.

Abstract: In the current market scenario, online customer reviews had a significant impact on boosting the sale of online products. Recently, there has been exponential growth in e-commerce industry owning to the online customer reviews. Over the years, researchers has observed the importance of online consumer reviews for purchasing online products. Hence, in this study, authors made an attempt to develop an efficient convolutional neural network (CNN) based classification model that aims to predict the usefulness of product reviews with higher accuracy on two different types of data sets (i.e., search product and experienced product). In our proposed study, to determine the usefulness of a review in terms of structural, linguistic, sentimental, lexical, and voting feature sets, we build a deep learning model to predict the review helpfulness as a binary classification problem. The performance of the proposed method is evaluated in terms of accuracy, precision, F1 score etc. and had been compared against the various leading machine learning (ML) state of art models viz., K-nearest neighbor (KNN), Linear regression (LR), Gaussian Naive Bays (GNB), Linear Discriminant Analysis (LDA) etc. The results demonstrate that CNN achieved better classification performance in comparison to other state of art models, with highest accuracy of 99.26% and 98.97%, precision of 99% and 99.01%, F1 score of 99% and 99.89%, AUC of 0.9999 and 0.9998, Average Precision (AP) of 0.9999 and 0.9997 and recall of 100% and 100% for two different amazon product datasets.

Keywords: CNN, reviews helpfulness, online reviews, machine learning, binary classification, reviews feature set

DOI: 10.3233/JIFS-223546

Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8851-8868, 2023