Amazon QA — Industrial & Scientific
A Amazon dataset of 12,136 QA pairs in Industrial & Scientific, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Industrial & Scientific. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
Cite this dataset at
amazon-qa-industrial-and-scientific.echo.directory
Analytics
unixTimeμ 1.3B ± 277.7M
min 0max 1.4B
has answer100.0% true
true 100.0%false 0.0%
is yes no47.6% true
true 47.6%false 52.4%
is open ended52.4% true
true 52.4%false 47.6%
5.5
Flesch–Kincaid grade
US school grade level
5.4
Automated readability index
useworkyesneedlikeproductfitdonwatergoodknowsuresizethinklongwantcomeworksvacuumgreatplastictimeunitinchessmallthankshopediameterholdbuymodelhelpspowerinchcleanboughtfinewayfindfilterfloorlittlecomessetmetalbetterhoseinsidehandleamazon
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Computed over the full corpus.
Record
- Catalog key
amazon_qa_Industrial_and_Scientific- Authors
- Wan, Mengting; McAuley, Julian
- Year
- 2016
- Source paper
- Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systems
- DOI / arXiv
- 10.1109/ICDM.2016.0060
- License
- No licence stated by the authors (citation requested)
- Records
- 12,136
- Languages
- en
- Collected
- 2013-2015 (2010s)
- Region
- Global (inferred: the platform is used worldwide)
Provenance
- Originally published at
- https://cseweb.ucsd.edu/~jmcauley/datasets.html#amazon_qa link checked
- Hosted copy
- Mirrored on echo.directory on 2026-10-02, 1.3 MB across 1 files. Checksums in MANIFEST.json.
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{Wan_2016,
title = {Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systems},
url = {http://dx.doi.org/10.1109/ICDM.2016.0060},
doi = {10.1109/icdm.2016.0060},
booktitle = {2016 IEEE 16th International Conference on Data Mining (ICDM)},
publisher = {IEEE},
author = {Wan, Mengting and McAuley, Julian},
year = {2016},
month = Dec,
pages = {489–498}
} APA
Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory
Wan, M., & McAuley, J. (2016). Amazon QA — Industrial & Scientific [Data set]. https://doi.org/10.1109/ICDM.2016.0060
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
Wan, Mengting, and Julian McAuley. Amazon QA — Industrial & Scientific. 2016, doi.org/10.1109/ICDM.2016.0060.