Version 1
: Received: 27 December 2023 / Approved: 28 December 2023 / Online: 28 December 2023 (15:20:08 CET)
Version 2
: Received: 13 January 2024 / Approved: 15 January 2024 / Online: 15 January 2024 (20:55:13 CET)
How to cite:
Malik, N.; Bilal, M. Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges. Preprints2023, 2023122210. https://doi.org/10.20944/preprints202312.2210.v1
Malik, N.; Bilal, M. Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges. Preprints 2023, 2023122210. https://doi.org/10.20944/preprints202312.2210.v1
Malik, N.; Bilal, M. Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges. Preprints2023, 2023122210. https://doi.org/10.20944/preprints202312.2210.v1
APA Style
Malik, N., & Bilal, M. (2023). Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges. Preprints. https://doi.org/10.20944/preprints202312.2210.v1
Chicago/Turabian Style
Malik, N. and Muhammad Bilal. 2023 "Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges" Preprints. https://doi.org/10.20944/preprints202312.2210.v1
Abstract
In recent years, e-commerce platforms have replaced conventional marketplaces. People are fast adopting online shopping since they can purchase from home. Online review sites allow customers to share their thoughts on products and services. Customers and businesses are increasingly relying on online reviews to assess and improve the quality of products. Existing literature uses Natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer evaluations, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges. This study covers 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis, review analysis, customer feedback and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the bulk of existing research relies on Amazon user reviews. Additionally, recent research has stressed the use of advanced techniques like Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates researchers' expanding interest and the field's continued growth. This survey additionally addresses open issues, providing future directions in online customer review analysis.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
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.
