How to Extract Data from Apple App Store?

Why Scraping App Store

The app store contains various categories like TV, movies and streaming, lifestyle, travel and food, books and magazines, social networking, and lots more. These apps also possess information related to ratings, reviews, downloads number, etc. Customer sentiments and feedback is essential to enhance the app’s functioning. Manually reading thousands of feedback is a tedious task. Hence, by automating the 0065traction process, you can easily extract any information in real-time.

Benefits of Scraping App Store

• Understanding customers’ requirements are the highest priority. Scraping reviews and analyzing them gives a better understanding of customers’ negative and positive feelings.
• Scraping enables tracking what’s trending. It, however, analyzes trends and new updates. Based on that, it picks the trending keywords that might help boost the app’s reach.
• It helps investigate the app’s popularity. By extracting information about trending and popular apps, developers can enhance the betterment of their apps.
• Scraping and analyzing data boosts the rate of success of specific marketing strategies.

Tools and Steps Involved in Apple App Store Data Extraction

Here we are using Scrapy, a web-scraping Python tool that will accomplish the task perfectly.

This general methodology was to move through each category and obtain the necessary information for later data analysis.

The information in the circle indicates that we were interested in scraping those from each app. We scraped more than 5000 applications with the below information:

• Name of the app
• Size in MB
• Category
• Compatibility
• Languages
• App Ratings (0-5)
• Age Ratings
• Price
• Total Ratings

The above image is the snippet of raw data obtained after scraping

Data Cleaning and Pre-processing

After we scraped the raw data, we used multiple tools in Python to ensure that our data were cleaned and formatted. The primary tool for cleaning is the Panda library. Next, we encapsulated all pre-processing code in a function for more elegant data analysis.

Data Analysis

The first thing we analyzed was the app size (MB) distribution. Our objective was to understand the app size density installed in the App Store and their ranges. We observed that most applications are between 50 and 100 MB. The below image illustrates this:

Next, we tried to correlate between category and app size. We found that the games were the heaviest apps installed in the app store.

During the scraping process, the rating feature was the most prominent metric. We then performed a wide range of exploratory data analyses based on rating and comparing them with other components associated with each app.

The above image depicts that the gaming category has the highest rating category on the app store.

Lastly, we looked into the most-rated new apps and found that Twitter and Reddit lead the top 10.

Scrape App Store Reviews using Python

Step 1: Installing and Setting Up packages

Using the Python package installer, first, install the app_store_scraper

Step 2: Get the App’s Name and ID

For the demo, here we are using the random app. Let’s take an example of the Slack app.

Now, import packages and then run the code.

Next, create an instance of the Appstore class and pass it in the argument’s app_name, country, and app_id.

The Slack variable stores all reviews. Run the below command to observe reviews stored in JSON format.

Step 3: Data Conversion from JSON

Now convert the data into JSON format to make it more readable and structured. Use the following code to do this:

Step 4: Dataframe Conversion to CSV

This final step converts the data frame into a comma-separated value format.

Save this Slack-app-review csv file into your folder, and you are ready to go.

Conclusion:

Thus, the ever-changing digital world generates a plethora of data daily. Millions and millions of apps hosts on the App Store, which has extensive market reach. Companies, however, require ratings and reviews data to modify and maintain their apps. With web scraping, you can easily understand users’ sentiment and by utilizing these understandings, companies can enhance their app functionalities. It also benefits companies to release new app updates and improve their marketing strategies.

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