Content Quality Analysis in Jupyter Notebooks
The NextGen documentation is still under construction.
Transform how you analyze content quality with the Acrolinx NextGen API Python SDK. This interactive notebook demonstrates how to integrate Acrolinx’s powerful content analysis directly into your data science workflow.
Run the notebook instantly in your browser—no setup required
Clone the notebook and run it locally with your own data
When to Use This Integration
This notebook approach is ideal when you need to:
- Analyze content quality programmatically within your data analysis workflow
- Generate quality reports for documentation, marketing copy, or technical content
- Integrate content checks into data science pipelines
- Visualize writing metrics alongside other analytics
- Batch process multiple documents for quality assessment
What You’ll Learn
In this tutorial, you’ll discover how to:
- Set up the Acrolinx Python SDK
- Submit content for quality analysis
- Poll for and retrieve detailed results
- Interpret quality scores across multiple dimensions
- Process specific issues and suggestions
Prerequisites
Before you begin, ensure you have:
- An Acrolinx NextGen API key (request beta access)
- Python 3.8 or higher
- Basic familiarity with Jupyter Notebooks
Step-by-Step Implementation
Setup and Installation
First, install the official Acrolinx NextGen API package and import the necessary modules:
Initialize the Client
Configure your Acrolinx client with your API key:
Store your API key securely. In Colab, use the secrets feature. In local notebooks, consider using environment variables.
Prepare Your Content
Set up the text you want to analyze. This example uses a business communication about remote work:
Configure Check Parameters
Choose your style guide, tone, and dialect for the analysis:
Available style guides include “microsoft”, “AP”, “chicago”, and custom guides. Check the API documentation for all options.
Example Output
When you run this notebook, you’ll see results like:
Understanding the Scores
- Quality Score: Overall content quality (0-100)
- Grammar Score: Correctness of grammar and spelling
- Style Guide Score: Adherence to the selected style guide
- Tone Score: Consistency with the specified tone
- Terminology Score: Proper use of domain-specific terms
Next Steps
Now that you’ve seen the Acrolinx Python SDK in action, you can:
1. Extend the Analysis
- Process multiple documents in a loop
- Export results to CSV or JSON for further analysis
- Create visualizations of quality metrics over time
2. Integrate with Your Workflow
- Add Acrolinx checks to your CI/CD pipeline
- Build automated quality reports for documentation
- Create custom functions for specific content types
Tips for Success
Performance: For large documents, consider splitting them into smaller chunks to improve processing time and avoid timeouts.
Error Handling: Always implement proper error handling and cleanup, especially when working with temporary files.
API Limits: Be mindful of rate limits. Implement exponential backoff when processing multiple documents.
Ready to improve your content quality? Request beta access to get started with the Acrolinx NextGen API!