Datasets:
license: mit
language:
- en
paperswithcode_id: embedding-data/Amazon-QA
pretty_name: Amazon-QA
Dataset Card for "Amazon-QA"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: http://jmcauley.ucsd.edu/data/amazon/qa/
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: Julian McAuley
- Size of downloaded dataset files:
- Size of the generated dataset:
- Total amount of disk used: 247 MB
Dataset Summary
This dataset contains Question and Answer data from Amazon, totaling around 1.4 million answered questions.
This dataset can be combined with Amazon product review data, available here, by matching ASINs in the Q/A dataset with ASINs in the review data. The review data also includes product metadata (product titles etc.).
Disclaimer: The team releasing Amazon-QA data did not upload the dataset to the Hub and did not write a dataset card. These steps were done by the Hugging Face team.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
Data Fields
Sample question (and answer):
{
"asin": "B000050B6Z",
"questionType": "yes/no",
"answerType": "Y",
"answerTime": "Aug 8, 2014",
"unixTime": 1407481200,
"question": "Can you use this unit with GEL shaving cans?",
"answer": "Yes. If the can fits in the machine it will despense hot gel lather. I've been using my machine for both , gel and traditional lather for over 10 years."
}
where
asin - ID of the product, e.g. B000050B6Z questionType - type of question. Could be 'yes/no' or 'open-ended' answerType - type of answer. Could be 'Y', 'N', or '?' (if the polarity of the answer could not be predicted). Only present for yes/no questions. answerTime - raw answer timestamp unixTime - answer timestamp converted to unix time question - question text answer - answer text
Data Splits
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
Modeling ambiguity, subjectivity, and diverging viewpoints in opinion question answering systems
Mengting Wan, Julian McAuley
International Conference on Data Mining (ICDM), 2016
Addressing complex and subjective product-related queries with customer reviews
Julian McAuley, Alex Yang
World Wide Web (WWW), 2016
Contributions
Thanks to Julian McAuley for adding this dataset.