Open Access

ARTICLE

Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

by K. Selvasheela^1,*, A. M. Abirami², Abdul Khader Askarunisa³

1 Department of Computer Science and Engineering, Sree Sakthi Engineering College,Coimbatore, Tamilnadu, 641104, India
2 Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamilnadu, 625015, India
3 Department of Computer Science and Engineering, Sethu Institute of Technology, Virudhunagar, Tamilnadu, 626115, India

* Corresponding Author: K. Selvasheela. Email:

Computer Systems Science and Engineering 2023, 44(3), 2537-2552. https://doi.org/10.32604/csse.2023.029148

Received 25 February 2022; Accepted 01 April 2022; Issue published 01 August 2022

Abstract

Nowadays, commercial transactions and customer reviews are part of human life and various business applications. The technologies create a great impact on online user reviews and activities, affecting the business process. Customer reviews and ratings are more helpful to the new customer to purchase the product, but the fake reviews completely affect the business. The traditional systems consume maximum time and create complexity while analyzing a large volume of customer information. Therefore, in this work optimized recommendation system is developed for analyzing customer reviews with minimum complexity. Here, Amazon Product Kaggle dataset information is utilized for investigating the customer review. The collected information is analyzed and processed by batch normalized capsule networks (NCN). The network explores the user reviews according to product details, time, price purchasing factors, etc., ensuring product quality and ratings. Then effective recommendation system is developed using a butterfly optimized matrix factorization filtering approach. Then the system’s efficiency is evaluated using the Rand Index, Dunn index, accuracy, and error rate.

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Keywords

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