Amazon Product and Google Locations Reviews
Donated on 2/24/2026
This is a preprocessed dataset derived from [Google Local Reviews](https://mcauleylab.ucsd.edu/public_datasets/gdrive/googlelocal/) and [Amazon Reviews](https://amazon-reviews-2023.github.io/) that contains time series data of counts of reviews from various categories per hour.
Dataset Characteristics
Time-Series
Subject Area
Business
Associated Tasks
Regression
Feature Type
Integer
# Instances
3055000
# Features
2
Dataset Information
Has Missing Values?
No
Introductory Paper
By Coen Adler, Yuxin Chang, Felix Draxler, Samar Abdi, Padhraic Smyth. 2026
Published in The Fourteenth International Conference on Learning Representations
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| Index | Other | Integer | Index Column | no | |
| ds | Feature | Categorical | Date Time | Date | no |
| y | Feature | Integer | # of Reviews per Hour | Count | no |
| unique_id | ID | Categorical | Integer ID for product/location category | ID | no |
0 to 4 of 4
Dataset Files
| File | Size |
|---|---|
| y_amazon-google-large.csv | 110.1 MB |
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset amazon_product_and_google_locations_reviews = fetch_ucirepo(id=1276) # data (as pandas dataframes) X = amazon_product_and_google_locations_reviews.data.features y = amazon_product_and_google_locations_reviews.data.targets # metadata print(amazon_product_and_google_locations_reviews.metadata) # variable information print(amazon_product_and_google_locations_reviews.variables)
Adler, C., Chang, Y., Draxler, F., Abdi, S., & Smyth, P. (2026). Amazon Product and Google Locations Reviews [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5SP7K.
Keywords
Creators
Coen Adler
ctadler@uci.edu
UC Irvine
Yuxin Chang
UC Irvine
Felix Draxler
UC Irvine
Samar Abdi
Padhraic Smyth
UC Irvine
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.