Version 1
: Received: 22 May 2023 / Approved: 23 May 2023 / Online: 23 May 2023 (10:58:37 CEST)
How to cite:
Elzeheiry, S.; Gab-Allah, W.A.; Mekky, N.; Elmogy, M. Sentiment Analysis for E-commerce Product Reviews: Current Trends and Future Directions. Preprints2023, 2023051649. https://doi.org/10.20944/preprints202305.1649.v1
Elzeheiry, S.; Gab-Allah, W.A.; Mekky, N.; Elmogy, M. Sentiment Analysis for E-commerce Product Reviews: Current Trends and Future Directions. Preprints 2023, 2023051649. https://doi.org/10.20944/preprints202305.1649.v1
Elzeheiry, S.; Gab-Allah, W.A.; Mekky, N.; Elmogy, M. Sentiment Analysis for E-commerce Product Reviews: Current Trends and Future Directions. Preprints2023, 2023051649. https://doi.org/10.20944/preprints202305.1649.v1
APA Style
Elzeheiry, S., Gab-Allah, W.A., Mekky, N., & Elmogy, M. (2023). Sentiment Analysis for E-commerce Product Reviews: Current Trends and Future Directions. Preprints. https://doi.org/10.20944/preprints202305.1649.v1
Chicago/Turabian Style
Elzeheiry, S., Nagham Mekky and Mohammed Elmogy. 2023 "Sentiment Analysis for E-commerce Product Reviews: Current Trends and Future Directions" Preprints. https://doi.org/10.20944/preprints202305.1649.v1
Abstract
Numerous goods and services are now offered through online platforms due to the recent growth of online transactions like e-commerce. Users have trouble locating the product that best suits them from the numerous products available in online shopping. Many studies in deep learning-based recommender systems (RSs) have focused on the intricate relationships between the attributes of users and items. Deep learning techniques have used consumer or item-related traits to improve the quality of personalized recommender systems in many areas, such as tourism, news, and e-commerce. Various companies, primarily e-commerce, utilize sentiment analysis to enhance product quality and effectively navigate today's business environment. Customer feedback regarding a product is gathered through sentiment analysis, which uses contextual data to split it into separate polarities. The explosive rise of the e-commerce industry has resulted in a large body of literature on e-commerce from different perspectives. Researchers have made an effort to categorize the recommended future possibilities for e-commerce study as the field has grown. There are several challenges in e-commerce, such as fake reviews, frequency of user reviews, advertisement click fraud, and code-mixing. In this review, we introduce an overview of the preliminary design for e-commerce. Second, the concept of deep learning, e-commerce, and sentiment analysis are discussed. Third, we represent different versions of the commercial dataset. Finally, we explain various difficulties facing RS and future research directions.
Keywords
E-commerce; Sentiment analysis; Natural language processing; Deep learning; Spam; Product reviews.
Subject
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.
