Amazon reviews

data category: Online reviews, e-commerce and recommender data · 1 paper in the last 7 days

← Most-used data · All Online reviews, e-commerce and recommender data datasets

Product and service reviews, ratings, purchases and user-item interactions.

Names searched for: Amazon review, Amazon product review, Amazon dataset

Key findings#

From Nov 2023 to Oct 2026, 166 archived papers mentioned Amazon reviews in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Sep 2025 (10).
Amazon reviews was mentioned in 6 papers in the last 30 days (2026-10-07 to 2026-11-05), ranking #51 of the 246 datasets mentioned.
By journal category: AI (4), Marketing (1) and Information Systems (1).
Most of them appeared in Knowledge-Based Systems (2), Journal of Marketing Analytics (1) and Expert Systems with Applications (1).
From 2017 to 2026 (so far), Amazon reviews was mentioned in 237 papers in the tracked journals, most of them in AI (157), information systems (32) and management (19).
60 of them were published in 2025, against 5 in 2017.
Since 2023, the institutions with the most of these papers were Zhejiang University (8), Hong Kong University of Science and Technology (4) and Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" (4).
The journals publishing most of them were IEEE Transactions on Knowledge and Data Engineering (24), Knowledge-Based Systems (14) and Expert Systems with Applications (14).
The most-cited of them is “Autoencoders and their applications in machine learning: a survey” (Artificial Intelligence Review, 2024), with 613 citations.
Papers use Amazon reviews most often with Yelp (48), Twitter / X (40) and Facebook (20); it has 34 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

Mentions over 36 months#

Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts and keywords; the full texts of earlier months are still being searched)
Trend	Nov 23–Oct 24	Nov 24–Oct 25	Nov 25–Oct 26	36 months
	42	60	64	166

Papers per year#

20172026 (so far)

Papers per publication year and category
Category	2017	2018	2019	2020	2021	2022	2023	2024	2025	2026 (so far)
AI	2	5	6	4	6	6	13	34	46	35
Information Systems	1	1	2	3	2	2	5	5	2	9
Management	0	0	1	1	2	2	2	3	6	2
Marketing	1	2	1	0	1	1	2	3	0	5
Operations Research	0	0	0	0	0	0	0	3	4	1
Economics	0	0	0	0	0	0	0	0	1	1
Finance	0	0	0	0	0	0	0	1	1	0
Health Economics and Policy	1	0	0	0	0	0	0	0	0	0
All categories	5	8	10	8	11	11	22	49	60	53

Where the papers come from#

Journals with the most papers, since 2023
#	Journal	Papers
1	IEEE Transactions on Knowledge and Data Engineering	24
2	Knowledge-Based Systems	14
3	Expert Systems with Applications	14
4	Artificial Intelligence Review	11
5	Machine Learning	8
6	Journal Of Big Data	8
7	Knowledge and Information Systems	7
8	Electronic Commerce Research	7
9	IEEE Transactions on Pattern Analysis and Machine Intelligence	6
10	Neural Computing and Applications	6

Institutions with the most papers, since 2023
#	Institution	Papers
1	Zhejiang University	8
2	Hong Kong University of Science and Technology	4
3	Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo"	4
4	Kühne Logistics University	4
5	National Research Council	4
6	Sichuan University	4
7	University of Electronic Science and Technology of China	4
8	Agency for Science, Technology and Research	3
9	Beijing Jiaotong University	3
10	Erasmus University Rotterdam	3

Countries with the most papers, since 2023
#	Country	Papers
1	China	64
2	United States	39
3	India	16
4	Italy	14
5	United Kingdom	12
6	Australia	11
7	Germany	11
8	France	11
9	Canada	10
10	Spain	9

Keywords with the most papers, since 2023
#	Keyword	Papers
1	Amazon reviews	52
2	sentiment analysis	22
3	large language models	19
4	recommender systems	17
5	online customer reviews	15
6	online reviews	15
7	graph neural networks	11
8	cross-domain recommendation	10
9	data sparsity	9
10	customer reviews	8

Most-cited papers#

Most-cited papers since 2017
#	Citations	Per month	Title	Journal	Published	Authors
1	613	18.5	Autoencoders and their applications in machine learning: a survey	Artificial Intelligence Review	2024-02-03	Kamal Berahmand, Fatemeh Daneshfar, Elaheh Sadat Salehi et al.
2	313	5.1	Creating and detecting fake reviews of online products	Journal of Retailing and Consumer Services	2021-09-20	Joni O. Salminen, Chandra Shekhar Kandpal, Ahmed Mohamed Kamel et al.
3	302	2.9	Explaining and predicting online review helpfulness: The role of content and reviewer-related signals	Decision Support Systems	2018-02-19	Michael Siering, Jan Muntermann, Balaji Rajagopalan
4	248	2.9	A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making	Industrial Marketing Management	2019-08-22	Erick Kauffmann, Jesús Peral, David Gil et al.
5	177	1.6	Weakly-Supervised Deep Embedding for Product Review Sentiment Analysis	IEEE Transactions on Knowledge and Data Engineering	2017-09-26	Wei Zhong Zhao, Ziyu Guan, Long Chen et al.

Archive counts by publication year: a paper counts when its title, abstract or keywords name the dataset, or when OpenAlex's full-text search finds it there (searched for the last 30 days' papers and, by a pass over the past years, for older ones). Names added to the list later are looked for in older papers as the archive is read again.

Used together with#

Papers use Amazon reviews most often with Yelp (48), Twitter / X (40) and Facebook (20); it has 34 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok. The whole network →

What papers use Amazon reviews together with (at least 3 papers; lift 1 = chance)
Used with	Type	Papers using both	Lift
Yelp	Dataset	48	26.3
Twitter / X	Dataset	40	1.8
Facebook	Dataset	20	1.3
MovieLens	Dataset	20	21.6
YouTube / TikTok	Dataset	19	2.1
Hugging Face Hub	Dataset	17	7.3
IMDb	Dataset	17	14.5
Location and social recommendation data (Gowalla, Foursquare, Yelp…	Dataset	17	16.4
ImageNet	Dataset	13	1.1
Wikipedia	Dataset	12	2.4
TripAdvisor	Dataset	11	5.0
Kaggle	Dataset	10	3.5

Papers mentioning Amazon reviews in the last 30 days, newest first.
Date	Title	Journal	Authors	Affiliations
2026-10-31	From product imagery to review expression: color-based visual aesthetic cues, textual sentiment, and consumer review engagement	Journal of Marketing Analytics	Vahideh Arghashi	Tokyo International University
2026-10-23	Relation-aware preference modeling with multimodal fusion for click-through rate prediction	Expert Systems with Applications	Aniqa Nawaz, Jianfeng Lu	Nanjing University of Science and Technology
2026-10-19	DReX: An explainable deep learning-based multimodal recommendation framework	Knowledge-Based Systems	Adamya Shyam, Venkateswara Rao Kagita, Bharti Rana et al.	University of Delhi; National Institute of Technology Warangal
2026-10-11	LiteSent: a lightweight adaptive multi-scale framework for social media and review-based sentiment classification	Knowledge-Based Systems	Dharmendra Dangi, Dheeraj Kumar Dixit, Amit Bhagat	Indian Institute of Information Technology Bhopal; Madhav Institute of Technology & Science; Maulana Azad National Institute of Technology
2026-10-08	More than words: how discrete emotions and image colorfulness influence the helpfulness of online reviews	Electronic Commerce Research	Junyi Yang, Xuecong Lu, Mei Yang et al.	McMaster University; State University of New York; University at Albany, State University of New York; …
2026-10-08	HAPO: Hierarchical affinity aware propagation for unsupervised graph anomaly detection	Pattern Recognition	Huahua Ding, Chaofan Dai, Haohao Zhou et al.	National University of Defense Technology

Cite this page

Scholar4AI. (2026). Amazon reviews. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/amazon-reviews

@misc{scholar4ai-datasets-amazon-reviews,
  author = {{Scholar4AI}},
  title = {Amazon reviews},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/amazon-reviews}},
  urldate = {2026-11-05},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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