Original scientific paper
https://doi.org/10.1080/00051144.2023.2295148
A novel framework for an intelligent deep learning based product recommendation system using sentiment analysis (SA)
Roshy Thomas
; Department of Computer Applications, Noorul Islam Centre for Higher Education, Kumaracoil, India
*
J. R. Jeba
; Department of Computer Applications, Noorul Islam Centre for Higher Education, Kumaracoil, India
* Corresponding author.
Abstract
Social media and e-commerce are the two most prominent and quickly expanding industries
today. These two areas exhibit the greatest influence on platform users. Numerous new people
sign up for these networks on a daily basis. This platform offers extremely quick user networking
and communication. These platforms are used to create an online product-based recommender
system that will help grow online business by recommending products. Online product recommendations are entirely dependent on the views, feedback, and comments of consumers.
Online recommender systems have become a regular part of consumers’ everyday routines, with
their widespread use observed in e-commerce, social networking platforms, and news websites.
This paper offers a novel framework for product recommendation based on sentiment analysis (SA) and collaborative filtering (CF). The SA was performed using an LSTM-based model. On
the basis of CF, two distinct recommendation systems were built. The proposed SA model was
integrated with the best recommendation system to enhance the recommendations. The experimental findings showed that the proposed system for product recommendation outperformed
the existing methods. The outcomes demonstrated the potential of combining CF and SA to
improve consumer satisfaction and product recommendation in e-commerce systems.
Keywords
E-commerce platform; user sentiments; user preferences; sentiment analysis; collaborative filtering; product recommendation;
Hrčak ID:
323034
URI
Publication date:
10.1.2024.
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