E-commerce
17 columns
200 rows
8 formats

Customer Product Review Dataset

This dataset provides a comprehensive view of customer product reviews, including detailed ratings, review text, sentiment analysis, and user demographics such as age, gender, and location. It enables advanced sentiment analysis, natural language processing, and customer satisfaction research, making it ideal for businesses seeking to understand product perception and improve customer experience.

opened 13 times
last updated 21 Jul 2025
by GoMask

The brief that made it

Sentiment analysis and NLP modeling for product feedback

Sample rows

preview · 8 of 200 rows · all 17 columns

review_idstring	sentiment_labelstring	sentiment_scorefloat	verified_purchaseboolean	customer_genderstring	customer_idstring	product_idstring	review_datedate	review_titlestring	ratinginteger	customer_ageinteger	customer_location_citystring	customer_location_statestring	customer_location_countrystring	helpful_votesinteger	not_helpful_votesinteger	review_textstring
R0001	positive	0.92	true	female	CUST1001	PROD201	2023-04-22	Great Value!	5	32	Austin	TX	US	27	1	Absolutely loved this product. Exceeded my expectations and the quality is fantastic.
R0002	negative	-0.83	true	male	CUST1002	PROD207	2023-05-15	Did not work as expected	1	45	Chicago	IL	US	3	4	The product malfunctioned after a week. Very disappointed with the purchase.
R0003	neutral	0.02	false	prefer not to say	CUST1003	PROD203	2023-07-01	It’s okay	3	37	Toronto	ON	CA	2	1	Average quality, nothing special but not too bad either.
R0004	positive	0.74	true	male	CUST1004	PROD210	2023-03-12	Solid choice	4	28	London	blank	UK	8	0	Solid performance and good value for money. Will consider buying again.
R0005	positive	0.88	true	female	CUST1005	PROD202	2023-06-10	Just what I needed	5	54	Sydney	NSW	AU	19	0	Arrived on time and works exactly as described. Highly recommend.
R0006	negative	-0.69	false	female	CUST1006	PROD203	2023-04-30	Poor build quality	2	29	Berlin	blank	DE	2	5	Started falling apart after a few uses. Not worth the price.
R0007	positive	0.95	true	male	CUST1007	PROD204	2023-07-20	Highly recommend	5	40	Denver	CO	US	23	1	Fantastic purchase! The features exceeded my expectations.
R0008	negative	-0.55	false	female	CUST1008	PROD205	2023-08-11	Not as described	2	blank	Miami	FL	US	4	6	Product description was misleading. Doesn’t have all the advertised features.
R0009	positive	0.73	true	male	CUST1009	PROD209	2023-05-30	Fast shipping	4	22	Paris	blank	FR	7	0	Came faster than expected and in perfect condition.
R0010	neutral	0.05	true	non-binary	CUST1010	PROD201	2023-03-18	Functional but basic	3	19	Dublin	blank	IE	1	2	It does what it’s supposed to but lacks advanced options.
R0011	positive	0.67	true	male	CUST1002	PROD202	2023-09-02	Great for the price	4	45	Chicago	IL	US	9	1	Affordable and does the job. Satisfied with the purchase.
R0012	negative	-0.93	false	female	CUST1011	PROD205	2023-08-21	Not happy	1	33	Madrid	blank	ES	1	10	Not worth the money. Will not buy again.
R0013	positive	0.61	true	male	CUST1012	PROD208	2023-07-04	Works as advertised	4	blank	blank	blank	blank	6	0	The product performs exactly as described. No complaints.
R0014	neutral	0.11	true	female	CUST1013	PROD207	2023-05-12	blank	3	27	Seattle	WA	US	4	3	Had a few issues but customer service was helpful.
R0015	negative	-1	false	male	CUST1014	PROD210	2023-06-23	Terrible product	1	61	Boston	MA	US	0	11	Stopped working after two days. Waste of money.
R0016	positive	0.81	true	female	CUST1015	PROD209	2023-07-22	Very useful	5	35	Houston	TX	US	15	0	Helps a lot with my daily tasks. Would buy again.
R0017	neutral	0.09	false	non-binary	CUST1016	PROD206	2023-08-12	Not bad	3	26	blank	blank	blank	2	1	It’s not the best, but it gets the job done.
R0018	positive	0.79	true	female	CUST1017	PROD201	2023-03-20	Easy to use	4	53	Los Angeles	CA	US	11	0	User-friendly and simple to set up.
R0019	negative	-0.49	false	male	CUST1018	PROD205	2023-07-29	Didn’t meet my needs	2	46	Melbourne	VIC	AU	0	3	Returned it after a week. Not compatible with my system.
R0020	positive	0.98	true	female	CUST1019	PROD202	2023-08-01	Love it!	5	blank	Vancouver	BC	CA	25	0	I use this every day and it never disappoints.

What the 200 rows show

from the 200-row sample

Negative (sentiment label) stands out: mean sentiment_score is -0.73, against 0.55 for the rest.

61%verified_purchase = true
0.14median sentiment_score
33customer location countries
42products
47customer location states
3.5median rating

sentiment_score197 rows, in bands of 0.2

Median 0.14, from -1.0 to 1.0.

customer_gender180 rows with a value · 20 left blank

female90
male71
prefer not to say10
non-binary9

17 columns by typefrom the column list below

string 10
integer 4
float 1
date 1
boolean 1

Columns

17 columns in four groups

blueprint · 17 columns
column	type	description	example
Text 10 columns
`review_id`	string	Unique identifier for each product reviewunique	R0001
`customer_id`	string	Unique identifier for the customer who wrote the review	CUST1001
`product_id`	string	Unique identifier for the reviewed product	PROD201
`review_title`	string	Short headline or summary of the reviewup to 200 · optional	Great Value!
`review_text`	string	Full text content of the customer review	Absolutely loved this pro…
`sentiment_label`	string	Categorical sentiment label assigned to the review text (e.g., positive, negative, neutral)positive · negative · neutral · optional	positive
`customer_gender`	string	Gender of the customer, if providedmale · female · non-binary · prefer not to say · optional	female
`customer_location_city`	string	City of the customer, if providedoptional	Austin
`customer_location_state`	string	State or province of the customer, if providedoptional	TX
`customer_location_country`	string	Country of the customer, if providedoptional	US
Numbers 5 columns
`rating`	integer	Customer rating for the product, typically on a 1-5 scale1 to 5	5
`sentiment_score`	float	Numerical sentiment score for the review text, typically between -1 (very negative) and 1 (very positive)-1 to 1 · optional	0.92
`customer_age`	integer	Age of the customer at the time of review13 to 120 · optional	32
`helpful_votes`	integer	Number of users who found the review helpful0 or more · optional	27
`not_helpful_votes`	integer	Number of users who did not find the review helpful0 or more · optional	1
Dates and times 1 column
`review_date`	date	Date when the review was submitted	2023-04-22
True or false 1 column
`verified_purchase`	boolean	Indicates whether the review is from a verified purchase	true

Use it for

An e-commerce dashboard
The verified_purchase rate, sentiment_score by sentiment_label and a breakdown of customer_gender. Excel, Power BI or Tableau.
Why do the 57 negative rows have a mean sentiment_score of -0.73?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Reviews200
R00010.92positive
R0002-0.83negative
R00030.02neutral
A software demo
Believable reviews with customer_id, product_id and review_date to fill a screen in front of a buyer.

This dataset200 rows17 columns

Yours10,000 rows17 columnscustomer_location_city: UK only

blueprint · customer-product-review-dataset

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 17 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Each row is one product review.
Includes product ID, user segment, rating, and review text.
No offensive content or spam reviews.
Ratings are between 1 and 5.

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. New accounts start with 25 free credits.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: customer-product-review-dataset

What should your data show?

No signup. No card.

Sample rows

What the 200 rows show

Columns

Use it for

An e-commerce dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in E-commerce

What should your data show?