How to Use Python to Scrape App Store Reviews

In today‘s hyper-competitive app economy, understanding your users is more critical than ever. With millions of apps vying for downloads, reviews serve as social proof, heavily influencing conversion rates and discoverability. As a result, app store reviews have become a gold mine of actionable insights for developers.

Consider these statistics:

• There are over 1.96 million apps available for download on the Apple App Store (Statista)
• The average app loses 77% of its daily active users within the first 3 days after install (Quettra)
• 79% of users will retry an app if it fails to load just 1-2 times (Dynatrace)
• A 1-star increase in app rating can lead to up to a 44% increase in downloads (Sensor Tower)

Clearly, reviews have a direct impact on an app‘s bottom line. But with hundreds of new reviews pouring in each day, sifting through the deluge of feedback quickly becomes unmanageable.

That‘s where web scraping comes in. By programmatically extracting review data, we can unlock insights at scale and spot issues before they snowball into 1-star disasters.

In this guide, we‘ll walk through how to use Python to scrape app store reviews – a skill every developer should have in their toolkit. Whether you‘re an indie dev with a single app or part of a larger product team, you‘ll learn how to gather, clean, and analyze review data to drive your roadmap.

Web Scraping 101

Before we dive into the code, let‘s cover some web scraping basics.

At its core, web scraping is the process of programmatically collecting data from websites. It typically involves the following steps:

1. Fetching the HTML content of a web page
2. Parsing the HTML to extract the desired data
3. Saving the extracted data in a structured format (spreadsheet, database, etc.)

Python has become the go-to language for web scraping thanks to its simple syntax and extensive collection of libraries. Some popular ones include:

Library	Description
BeautifulSoup	Parse and extract data from HTML and XML
Scrapy	Framework for writing web crawlers and extracting structured data
Selenium	Automate web browsers (click buttons, fill forms)
Requests	Make HTTP requests and retrieve web page content

For our app store scraping project, we‘ll be using a specialized Python library called app_store_scraper. As the name implies, it‘s specifically designed for extracting data from the Apple App Store.

Before we start scraping, a quick note on ethics. While web scraping is legal, it‘s important to be a good citizen and respect website owners. Here are some best practices to follow:

• Check the robots.txt file for a site‘s scraping rules
• Set a reasonable request rate to avoid overloading servers
• Don‘t scrape copyrighted content or personal information
• Use scraped data for analysis, not publishing

With those guidelines in mind, let‘s start collecting some app reviews!

Setting Up Your Environment

First, make sure you have Python installed. We‘ll be using Python 3 in this guide, so if you‘re still on Python 2, now‘s a great time to upgrade. You can download the latest version from the official Python website.

Next, let‘s install the app_store_scraper library using pip, Python‘s package manager. Open a terminal and run:

pip3 install app-store-scraper

We‘ll also need the pandas library for data analysis later on:

pip3 install pandas 

Now that we have our tools ready, it‘s time to find an app to scrape. For this example, we‘ll use Slack, but feel free to follow along with your own app.

To scrape reviews, we‘ll need the app‘s ID and country code. The easiest way to find these is from the app‘s URL in the App Store:

https://apps.apple.com/us/app/slack/id618783545

From this, we can see Slack‘s app ID is 618783545 and the country code is us for the United States.

Scraping Reviews

With our app info in hand, we‘re ready to start scraping! Create a new Python file called scraper.py and add the following code:

from app_store_scraper import AppStore

slack = AppStore(country="us", app_name="slack", app_id=618783545)
slack.review(how_many=2000)

print(slack.reviews)

Here‘s what‘s happening:

1. We import the AppStore class from app_store_scraper
2. We create a new AppStore instance called slack, passing in the country, app name, and ID
3. We call the review() method, specifying we want to scrape 2000 reviews
4. We print out the scraped review data

Run the script with:

python3 scraper.py

After a few seconds (or minutes depending on your connection speed), you should see a JSON dump of 2000 Slack reviews!

[
    {
        "userName": "CoffeeDeprived",
        "rating": 5,
        "date": "2022-03-02T23:03:34Z",
        "review": "I can‘t imagine work without Slack...",
        "title": "Essential for remote work" 
    },
    {
        "userName": "PugLover27", 
        "rating": 4,
        "date": "2022-03-01T07:21:12Z",
        "review": "Generally reliable but occasional glitches...",
        "title": "Mostly stable"
    },
    ...
]

Congrats, you‘ve just scraped your first batch of reviews! But we‘re just getting started. Let‘s keep going and clean up this data into a more usable format.

Cleaning Scraped Data

While our scraped JSON is structured, it‘s not the easiest to work with. To make our lives easier down the road, let‘s convert it to a pandas DataFrame.

Add this code to your scraper.py file:

import pandas as pd

df = pd.DataFrame(slack.reviews)
print(df.head())

We import pandas, convert the reviews to a DataFrame, and print out the first few rows. The head() method is a handy way to take a peek at your data without printing the whole thing.

You should get a nicely formatted table like this:

	userName	rating	date	review	title
0	CoffeeDeprived	5	2022-03-02T23:03:34Z	I can‘t imagine work without Slack…	Essential for remote work
1	PugLover27	4	2022-03-01T07:21:12Z	Generally reliable but occasional glit…	Mostly stable
2	SalesGuru	4	2022-02-28T17:41:51Z	Great for client communication! Locati…	Perfect for sales teams
3	DesignMaven	2	2022-02-26T09:17:26Z	Since the latest update, the app keeps…	Buggy and unstable
4	CatMom5	5	2022-02-25T14:33:20Z	Slack is the glue that holds our remot…	Can‘t live without it

So much easier to read! DataFrames allow us to manipulate data by row/column. For example, we can calculate the average rating:

print(df["rating"].mean())

# Output: 4.21

Or count the total number of reviews:

print(len(df))

# Output: 2000

In the next section, we‘ll cover some more advanced analysis. But for now, let‘s save our cleaned data to disk.

To export to CSV, add this line:

df.to_csv("slack_reviews.csv", index=False)

Now in your directory, you should have a CSV file called slack_reviews.csv containing all 2000 reviews. This is a great format for sharing data with non-technical teammates.

Analyzing Reviews

Now for the fun part – finding insights! Reviews are a treasure trove of both quantitative and qualitative data. Let‘s start by visualizing the ratings distribution.

We can use pandas‘ built-in plotting to quickly generate a bar chart:

import matplotlib.pyplot as plt

df["rating"].value_counts().plot(kind="bar") 
plt.title("Ratings Distribution")
plt.xlabel("Rating")
plt.ylabel("Number of Reviews")
plt.show()

Here‘s what that looks like:

Looks like the majority of ratings are 4 and 5 stars, which is a great sign. But what about those 1-star reviews? Let‘s use a word cloud to see what words are most frequent in the negative reviews.

First, install the wordcloud package:

pip3 install wordcloud

Now add this code to create a word cloud from 1-star reviews:

from wordcloud import WordCloud

neg_reviews = df[df["rating"] == 1]["review"]
text = " ".join(neg_reviews)

wordcloud = WordCloud(width=800, height=800, background_color="white").generate(text)

plt.figure(figsize=(8, 8), facecolor=None)
plt.imshow(wordcloud)
plt.axis("off")
plt.tight_layout(pad=0)

plt.show()

Yikes – it looks like crashes, freezing, and bugs are common complaints in the 1-star reviews. These are high-priority issues that should be investigated ASAP.

Of course, these are just a few basic analyses you can do with review data. Some other techniques you might want to explore:

• Sentiment analysis to quantify the emotion of review text
• Topic modeling to discover themes across reviews
• Compare review metrics over time or across app versions

The possibilities are endless! For even more inspiration, check out how these companies are leveraging app review data:

• Roblox increased player retention by 10% by using review insights to identify top gameplay issues (Sensor Tower)
• DoorDash grew revenue by 56% after revamping their app based on review feedback (AppFollow)
• Lyft fixed a major login issue that was mentioned in 67% of negative reviews, reducing 1-star reviews by 40% (Apteligent)

Next Steps

Congratulations! You now have the skills to scrape and analyze app store reviews using Python. But don‘t stop here – integrate review mining into your development workflow to continuously gather user feedback. Some ideas:

• Set up a daily cron job to automatically scrape new reviews
• Create a real-time dashboard displaying key review metrics
• Use review insights to prioritize bug fixes and feature requests

To dive deeper into the world of web scraping with Python, check out these resources:

• Automate the Boring Stuff with Python: Web Scraping
• Beautiful Soup Documentation
• Scrapy Tutorial

Happy scraping!

References

• Apteligent. (2017, May). Using app store review data to identify high-impact bugs and boost ratings
• AppFollow. (2022). Customer Spotlight: DoorDash Boosts Revenue Increase and User Satisfaction with AppFollow
• Dynatrace. (2021). Mobile App Attention Index 2021
• Quettra. (2015). Mobile Apps: What‘s A Good Retention Rate?
• Sensor Tower. (2021). Review Insights: A Key Driver in Maximizing Mobile Growth
• Statista. (2022). Number of available applications in the Apple App Store from 1st quarter 2015 to 1st quarter 2022