The Challenge
Since the launch of Helldivers 2, the game has experienced various performance issues, crashes, and connectivity problems reported by players on Steam. The challenge was to develop a tool that could help the development team at Arrowhead Studios identify and address these issues efficiently to maintain high player satisfaction and engagement.
The Approach
1. Filterable Dates: Implemented a feature to view issues from specific periods, helping pinpoint when certain problems started and track improvements over time.
2. Sentiment Analysis: Analyzed the sentiment of user reviews and feedback to highlight overall player mood and satisfaction, crucial for maintaining a positive community.
3. Issue Categorization: Categorized issues into crashes, connectivity problems, content issues, and community interactions to provide targeted insights for developers.
4. Data Integration and Visualization: Used Python for scripting, data scraping, and calling the Steamworks API to gather real-time data and feedback. Employed Tableau for visualizing data and insights, enabling the team to make informed decisions quickly.
5. Automation with Apache Airflow: Utilized Apache Airflow for scheduling tasks, ensuring consistent data collection and analysis every week to keep the feedback loop current and relevant.
The Outcome
The project resulted in a comprehensive analytics tool that provides real-time insights into player feedback and performance issues for Helldivers 2. The development team at Arrowhead Studios can now swiftly identify and address issues, significantly improving the overall gaming experience.
Users Can:
- Filter issues by specific periods to track the origin and improvements of problems.
- View sentiment analysis of user reviews to gauge overall player satisfaction.
- Identify and categorize issues such as crashes, connectivity problems, and content feedback.
- Access real-time data and insights through interactive visualizations.
Key Features
- Filterable Dates: Allows the team to view issues from specific periods, helping pinpoint when certain problems started and track improvements over time.
- Sentiment Analysis: Analyzes the sentiment of user reviews and feedback, highlighting overall player mood and satisfaction.
- Issue Categorization: Categorizes issues into crashes, connectivity problems, content feedback, and community interactions.
- Data Integration and Visualization: Uses Python for scripting, data scraping, and calling the Steamworks API. Employs Tableau for data visualization.
- Automation with Apache Airflow: Utilizes Apache Airflow for scheduling tasks, ensuring consistent data collection and analysis.
Technologies Used
- Python: For scripting, data scraping, and calling the Steamworks API.
- BERTopic: For topical modeling to identify the top topics in user feedback.
- Tableau: For data visualization.
- Apache Airflow: For scheduling tasks and automating data collection and analysis.