Amazon Movie Reviews
A Amazon.com movie product pages dataset of 7,911,696 movie reviews spanning 1997 to 2012, collected by McAuley & Leskovec via Amazon product review pages (2013). Each record contains product ID, reviewer user ID, reviewer profile name, helpfulness votes (numerator/denominator), star rating (1-5), Unix timestamp, review summary, and full review text. One of the largest Amazon review datasets publicly available — 8 million movie reviews with full text, helpfulness votes, and star ratings spanning 15 years of Amazon movie reviews.. Suitable for sentiment analysis, review helpfulness prediction, recommendation systems, user expertise modeling, longitudinal review behavior analysis.
Cite this dataset at
amazon-movies.echo.directory
Analytics
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Sampled: text metrics from 50K of 100K records; topic model from 20K.
Record
- Catalog key
amazon-movies- Authors
- McAuley, Julian J.; Leskovec, Jure
- Year
- 2013
- Source paper
- From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews
- DOI / arXiv
- 10.1145/2488388.2488466
- License
- Academic use (SNAP — Stanford Network Analysis Project) (LicenseRef-Research-Only)
- Records
- 7,911,696
- Nodes are
- unknown
- Edges are
- unknown
- Text corpus
- movie review text
- Languages
- en
- Collected
- 1997-08 to 2012-10 (1990s, 2000s, 2010s)
- Region
- Global (inferred: the platform is used worldwide)
Provenance
- Originally published at
- https://snap.stanford.edu/data/web-Movies.html link checked
- Hosted copy
- Mirrored on echo.directory on 2026-10-02, 3.1 GB across 1 files. Checksums in MANIFEST.json.
Citations
From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews
Julian McAuley, J. Leskovec
Journal Article · The Web Conference · 2013
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Shijie Zhang, Hongzhi Yin et al. · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval · 2020 · cited 221× - Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach
Jing-Jing Xu, Xu Sun et al. · Annual Meeting of the Association for Computational Linguistics · 2018 · cited 215× - SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks
Ke Wang, Xiaojun Wan · International Joint Conference on Artificial Intelligence · 2018 · cited 199× - H2-FDetector: A GNN-based Fraud Detector with Homophilic and Heterophilic Connections
Fengzhao Shi, Yanan Cao et al. · The Web Conference · 2022 · cited 179×
Citation data from Semantic Scholar, retrieved 2026-10-19.
How to cite
BibTeX
@misc{echo2026,
title = {ECHO: Exploratory Compendium of Humanity Online},
author = {Switzer, Braden and Zafarani, Reza},
year = {2026},
url = {https://echo.directory},
note = {Syracuse University}
}
@inproceedings{mcauley2013amateurs,
title = {From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews},
author = {McAuley, Julian J. and Leskovec, Jure},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {897--908},
year = {2013},
doi = {10.1145/2488388.2488466}
} APA
Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory
McAuley, J. J., & Leskovec, J. (2013). Amazon Movie Reviews [Data set]. https://doi.org/10.1145/2488388.2488466
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
McAuley, Julian J., and Jure Leskovec. Amazon Movie Reviews. 2013, doi.org/10.1145/2488388.2488466.