get_playstore_reviews¶

get_playstore_reviews(app_id, country, language)

Description¶

GET Google Play Store Reviews of an app (using google-play-scraper)

app_id can be found in the url of the app on the play store. For example for url https://play.google.com/store/apps/details?id=com.fpe.comptenickel&hl=en&pli=1, the app_id is com.fpe.comptenickel
country is the two-letter country iso code of the device where the app was downloaded
language is the two-letter language iso code of the review

The function will return the 600 newest reviews as json.

Examples¶

Call or Deploy get_playstore_reviews ?

Call get_playstore_reviews directly

The easiest way to use bigfunctions

get_playstore_reviews function is deployed in 39 public datasets for all of the 39 BigQuery regions.
It can be called by anyone. Just copy / paste examples below in your BigQuery console. It just works!
(You need to use the dataset in the same region as your datasets otherwise you may have a function not found error)

Public BigFunctions Datasets

Region	Dataset
`eu`	`bigfunctions.eu`
`us`	`bigfunctions.us`
`europe-west1`	`bigfunctions.europe_west1`
`asia-east1`	`bigfunctions.asia_east1`
...	...

Deploy get_playstore_reviews in your project

Why deploy?

You may prefer to deploy get_playstore_reviews in your own project to build and manage your own catalog of functions.
This is particularly useful if you want to create private functions (for example calling your internal APIs).
Get started by reading the framework page

Deployment

get_playstore_reviews function can be deployed with:

pip install bigfunctions
bigfun get get_playstore_reviews
bigfun deploy get_playstore_reviews

GET reviews written in English for Nickel App installed on French devices

EUUSeurope-west1

select bigfunctions.eu.get_playstore_reviews('com.fpe.comptenickel', 'fr', 'en')

select bigfunctions.us.get_playstore_reviews('com.fpe.comptenickel', 'fr', 'en')

select bigfunctions.europe_west1.get_playstore_reviews('com.fpe.comptenickel', 'fr', 'en')

+--------------------------------------------------------------------------------------------------------------------------------------------------------------+
| reviews                                                                                                                                                      |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------+
| [
  {
    appVersion: "2.90.0"
    at: "Thu, 19 Sep 2024 10:44:05 GMT",
    content: "...",
    repliedAt: ...,
    replyContent: ...,
    ...
  },
  ...
]
 |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------+

Need help or Found a bug using get_playstore_reviews?

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Report a bug about get_playstore_reviews

If the function does not work as expected, please

report a bug so that it can be improved.
or open the discussion with the community on Slack.

We also provide professional suppport.

Use cases¶

A market research team wants to analyze user sentiment towards a specific mobile application (e.g., "Nickel App" with app ID com.fpe.comptenickel) across different regions. They want to understand how French users who have downloaded the app and written their review in English perceive the app. To achieve this, they would use the get_playstore_reviews BigQuery function with the following parameters:

app_id: com.fpe.comptenickel
country: fr (France)
language: en (English)

They would then execute a query like this (adjusting the dataset bigfunctions.us to match their BigQuery region):

SELECT * FROM UNNEST(JSON_EXTRACT_ARRAY(bigfunctions.us.get_playstore_reviews('com.fpe.comptenickel', 'fr', 'en'), '$.reviews')) AS review;

This query retrieves the reviews as a JSON array, then unnests the array so each review is a separate row. The team can then perform further analysis on the content, score, and other fields within each review to gauge user sentiment, identify common themes in positive or negative feedback, and understand the overall user experience for this specific user segment. This information can then be used to inform product development, marketing strategies, and customer support efforts.

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