Building App-Review-Genius: Automating iOS Insights with GitHub Actions

import requests
from bs4 import BeautifulSoup

def scrape_app_metadata(app_id):
    url = f"https://itunes.apple.com/us/app/id{app_id}"
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    
    app_data = {
        'title': soup.find('h1').text,
        'developer': soup.find('a', class_='link').text,
        'release_date': soup.find('time')['datetime'],
        'genres': [genre.text for genre in soup.find_all('a', class_='genre')]
    }
    
    return app_data

name: Daily App Review Collection

on:
  schedule:
    - cron: '0 0 * * *'  # Runs daily at midnight

jobs:
  collect_reviews:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v2

      - name: Run data collection script
        run: python get-top100-app-daily.py

import openai

def categorize_reviews(reviews):
    categorized_reviews = {}
    for review in reviews:
        response = openai.ChatCompletion.create(
            model="gpt-4",
            messages=[
                {"role": "user", "content": f"Categorize this review: {review}"}
            ]
        )
        category = response['choices'][0]['message']['content']

## Lessons from the Trenches

Based on your project history and README, here are some key technical lessons learned, what worked well, what you might do differently, and advice for others:

### Key Technical Lessons Learned
1. **Automation is Key**: Utilizing GitHub Actions for automating the data collection process proved to be efficient. It allows for continuous data gathering without manual intervention, which is crucial for keeping the dataset up-to-date.
  
2. **Diverse Data Sources**: Leveraging multiple sources (Wayback Machine, Google Search, sitemaps) for app discovery and historical data collection is essential. This approach helps in building a comprehensive dataset.

3. **Rate Limiting and Throttling**: Implementing sleep intervals between requests (e.g., 2 seconds for collecting reviews) is vital to avoid being blocked by the app store or search engines. This ensures sustainable scraping practices.

4. **Data Structuring**: Structuring the collected data effectively (e.g., app details, reviews, developer info) is crucial for analysis. Using a consistent format makes it easier to perform insights and analytics later.

5. **Keyword Expansion**: Using keywords to discover related apps and reviews can significantly enhance the dataset. This method can lead to discovering niche apps that may not be in the top rankings but have valuable user feedback.

### What Worked Well
1. **Daily Tracking of Top Apps**: The implementation of a daily script to track the top 100 apps in the health category has been effective. This allows for timely insights into market trends and user preferences.

2. **Integration with GPT-4**: Utilizing GPT-4 for categorizing reviews into feedback types is a powerful way to analyze user sentiment and feature requests, although it’s still in the testing phase.

3. **Historical Data Collection**: The ability to collect historical reviews for apps with a large number of reviews has provided a rich dataset for analysis, allowing for trend identification over time.

### What You'd Do Differently
1. **Testing and Iteration**: Prioritize testing the historical data mining script and iterating based on the results. Early testing can help identify potential issues before scaling up the data collection.

2. **Error Handling**: Implement more robust error handling and logging mechanisms to track issues during scraping. This will help in diagnosing problems quickly and maintaining data integrity.

3. **User Feedback Loop**: Consider creating a feedback loop where users of the dataset can provide insights or request additional features. This could guide future development and enhancements.

### Advice for Others
1. **Start Small**: If you're new to data scraping, start with a small subset of apps or reviews. Gradually scale up as you become more comfortable with the tools and techniques.

2. **Respect Terms of Service**: Always review and respect the terms of service of the platforms you are scraping. This helps avoid legal issues and ensures ethical data collection practices.

3. **Documentation**: Maintain thorough documentation of your processes, scripts, and findings. This will be invaluable for future reference and for anyone else who may work on the project.

4. **Community Engagement**: Engage with the developer community on platforms like GitHub. Sharing your findings and learning from others can lead to valuable insights and collaborations.

5. **Focus on Insights**: While collecting data is important, focus on deriving actionable insights from the data. This will make your project more impactful and relevant to your goals in the health and gaming sectors.

By following these lessons and advice, you can enhance your project and make it more effective in achieving your goals in the intersection of gaming and healthcare.

## What's Next?

### Conclusion for App-Review-Genius

As we reflect on the journey of App-Review-Genius, we are excited to share our current project status and future development plans. Over the past two years, we have made significant strides in our app review scraping capabilities, particularly with our focus on the iOS app landscape. Our recent implementation of a daily tracking system for the top 100 health apps has streamlined our data collection process, allowing us to gather insights effortlessly while we sleep. The integration of GitHub Actions has minimized manual effort, enabling us to focus on analysis and insights.

Looking ahead, our development plans are ambitious. We aim to enhance our historical data mining capabilities and refine our insights generation using advanced AI techniques, including the categorization of reviews with GPT-4. We also plan to expand our focus beyond health apps to include other categories, thereby broadening our dataset and insights. Additionally, we are eager to test and improve the scripts we have developed, ensuring they are robust and effective in delivering valuable data.

We invite contributors to join us on this exciting journey. Whether you are a developer, data analyst, or simply passionate about app reviews, your insights and contributions can help us refine our tools and expand our reach. Together, we can create a comprehensive resource that benefits developers, marketers, and users alike.

In closing, the journey of App-Review-Genius has been both challenging and rewarding. It has been a side project fueled by passion and curiosity, and we are grateful for the progress we have made. As we continue to evolve and grow, we look forward to the collaborative efforts that will shape the future of this project. Let’s harness the power of app reviews to drive innovation and improve user experiences in the app ecosystem. Join us, and let’s make a difference together!
## Project Development Analytics
### timeline gant

![Commit timelinegant](https://daily.borninsea.com/assets/app-review-genius-timeline_chart.png)


### Commit Activity Heatmap
This heatmap shows the distribution of commits over the past year:

![Commit Heatmap]()

### Contributor Network
This network diagram shows how different contributors interact:

![Contributor Network](https://daily.borninsea.com/assets/app-review-genius-contribution_network.png)

### Commit Activity Patterns
This chart shows when commits typically happen:

![Commit Activity](https://daily.borninsea.com/assets/app-review-genius-commit_activity.png)

### Code Frequency
This chart shows the frequency of code changes over time:

![Code Frequency](https://daily.borninsea.com/assets/app-review-genius-code_frequency.png)



* Repository URL: [https://github.com/wanghaisheng/app-review-genius](https://github.com/wanghaisheng/app-review-genius)
* Stars: **0**
* Forks: **1**