Version 1
: Received: 20 December 2023 / Approved: 21 December 2023 / Online: 22 December 2023 (07:01:39 CET)
How to cite:
Abdullah, S.; Ashraf, H.; Jhanjhi, N. Online Reviews Classification and Summarization: A Comprehensive Analysis. Preprints2023, 2023121682. https://doi.org/10.20944/preprints202312.1682.v1
Abdullah, S.; Ashraf, H.; Jhanjhi, N. Online Reviews Classification and Summarization: A Comprehensive Analysis. Preprints 2023, 2023121682. https://doi.org/10.20944/preprints202312.1682.v1
Abdullah, S.; Ashraf, H.; Jhanjhi, N. Online Reviews Classification and Summarization: A Comprehensive Analysis. Preprints2023, 2023121682. https://doi.org/10.20944/preprints202312.1682.v1
APA Style
Abdullah, S., Ashraf, H., & Jhanjhi, N. (2023). Online Reviews Classification and Summarization: A Comprehensive Analysis. Preprints. https://doi.org/10.20944/preprints202312.1682.v1
Chicago/Turabian Style
Abdullah, S., Humaira Ashraf and NZ Jhanjhi. 2023 "Online Reviews Classification and Summarization: A Comprehensive Analysis" Preprints. https://doi.org/10.20944/preprints202312.1682.v1
Abstract
The classification and summarization of online reviews using machine learning and deep learningbased approaches are the subject of this systematic literature review. Consumers, companies, and researchers can now find vital information from online reviews. Automatically categorizing and summarizing these reviews can offer useful information and facilitate decision-making. The purpose of this study is to offer a thorough overview of the current methodology, approaches, and developments in this area. The review concentrates on machine learning-based methods for categorization and summarizing tasks, highlighting their advantages, drawbacks, and new developments. This systematic review provides insights into the most recent methodologies and indicates prospective directions for further investigation by synthesizing the data from a wide range of pertinent research publications
Keywords
online reviews, classification, summarization, machine learning, systematic literature review.
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
