Content Calendar, Content Gap Analysis, and Content Optimization
This commit is contained in:
ajaysi
249
lib/ai_seo_tools/content_gap_analysis/utils/README.md
Normal file
249
lib/ai_seo_tools/content_gap_analysis/utils/README.md
Normal file
@@ -0,0 +1,249 @@
|
||||
# Content Gap Analysis Utils
|
||||
|
||||
This directory contains utility modules that power the Content Gap Analysis tool. These modules provide core functionality for data collection, processing, analysis, and storage.
|
||||
|
||||
## Directory Structure
|
||||
|
||||
```
|
||||
utils/
|
||||
├── README.md
|
||||
├── ai_processor.py # AI-powered content analysis and processing
|
||||
├── content_parser.py # Content structure parsing and analysis
|
||||
├── data_collector.py # Website data collection and processing
|
||||
└── storage.py # Analysis results storage and retrieval
|
||||
```
|
||||
|
||||
## Module Descriptions
|
||||
|
||||
### 1. AI Processor (`ai_processor.py`)
|
||||
|
||||
The AI Processor module enhances content analysis using AI techniques. It provides intelligent analysis of website content, competitor data, and keyword research.
|
||||
|
||||
#### Key Features:
|
||||
- Content quality assessment
|
||||
- Topic analysis and clustering
|
||||
- Performance metrics analysis
|
||||
- Strategic recommendations generation
|
||||
- Progress tracking for analysis tasks
|
||||
|
||||
#### Main Components:
|
||||
- `AIProcessor`: Main class for AI-powered analysis
|
||||
- `ProgressTracker`: Tracks analysis progress and status
|
||||
|
||||
#### Usage Example:
|
||||
```python
|
||||
from utils.ai_processor import AIProcessor
|
||||
|
||||
processor = AIProcessor()
|
||||
analysis = processor.analyze_content({
|
||||
'url': 'https://example.com',
|
||||
'industry': 'technology',
|
||||
'content': content_data
|
||||
})
|
||||
```
|
||||
|
||||
### 2. Content Parser (`content_parser.py`)
|
||||
|
||||
The Content Parser module handles the parsing and analysis of website content structure. It provides detailed insights into content organization and quality.
|
||||
|
||||
#### Key Features:
|
||||
- Content structure analysis
|
||||
- Text statistics calculation
|
||||
- Topic extraction
|
||||
- Readability analysis
|
||||
- Content hierarchy analysis
|
||||
|
||||
#### Main Components:
|
||||
- `ContentParser`: Main class for content parsing and analysis
|
||||
|
||||
#### Usage Example:
|
||||
```python
|
||||
from utils.content_parser import ContentParser
|
||||
|
||||
parser = ContentParser()
|
||||
structure = parser.parse_structure({
|
||||
'main_content': content,
|
||||
'html': html_content,
|
||||
'headings': headings_data
|
||||
})
|
||||
```
|
||||
|
||||
### 3. Data Collector (`data_collector.py`)
|
||||
|
||||
The Data Collector module is responsible for gathering website data for analysis. It handles web scraping and data extraction.
|
||||
|
||||
#### Key Features:
|
||||
- Website content collection
|
||||
- Meta data extraction
|
||||
- Heading structure analysis
|
||||
- Link and image extraction
|
||||
- Error handling and retry logic
|
||||
|
||||
#### Main Components:
|
||||
- `DataCollector`: Main class for data collection
|
||||
|
||||
#### Usage Example:
|
||||
```python
|
||||
from utils.data_collector import DataCollector
|
||||
|
||||
collector = DataCollector()
|
||||
data = collector.collect('https://example.com')
|
||||
```
|
||||
|
||||
### 4. Storage (`storage.py`)
|
||||
|
||||
The Storage module manages the persistence and retrieval of analysis results. It provides a robust database interface for storing and accessing analysis data.
|
||||
|
||||
#### Key Features:
|
||||
- Analysis results storage
|
||||
- Historical data management
|
||||
- Recommendation tracking
|
||||
- User-specific analysis storage
|
||||
- Error handling and rollback support
|
||||
|
||||
#### Main Components:
|
||||
- `ContentGapAnalysisStorage`: Main class for storage operations
|
||||
|
||||
#### Usage Example:
|
||||
```python
|
||||
from utils.storage import ContentGapAnalysisStorage
|
||||
|
||||
storage = ContentGapAnalysisStorage(db_session)
|
||||
analysis_id = storage.save_analysis(
|
||||
user_id=1,
|
||||
website_url='https://example.com',
|
||||
industry='technology',
|
||||
results=analysis_results
|
||||
)
|
||||
```
|
||||
|
||||
## Integration Points
|
||||
|
||||
### 1. Website Analysis Integration
|
||||
```python
|
||||
from utils.data_collector import DataCollector
|
||||
from utils.content_parser import ContentParser
|
||||
from utils.ai_processor import AIProcessor
|
||||
|
||||
# Collect data
|
||||
collector = DataCollector()
|
||||
data = collector.collect(url)
|
||||
|
||||
# Parse content
|
||||
parser = ContentParser()
|
||||
structure = parser.parse_structure(data)
|
||||
|
||||
# Process with AI
|
||||
processor = AIProcessor()
|
||||
analysis = processor.analyze_content({
|
||||
'url': url,
|
||||
'content': structure
|
||||
})
|
||||
```
|
||||
|
||||
### 2. Storage Integration
|
||||
```python
|
||||
from utils.storage import ContentGapAnalysisStorage
|
||||
|
||||
# Store analysis results
|
||||
storage = ContentGapAnalysisStorage(db_session)
|
||||
analysis_id = storage.save_analysis(
|
||||
user_id=user_id,
|
||||
website_url=url,
|
||||
industry=industry,
|
||||
results=analysis_results
|
||||
)
|
||||
|
||||
# Retrieve analysis
|
||||
results = storage.get_analysis(analysis_id)
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
|
||||
All modules implement comprehensive error handling:
|
||||
|
||||
1. **Data Collection Errors**
|
||||
- Network timeouts
|
||||
- Invalid URLs
|
||||
- Access restrictions
|
||||
- Parsing errors
|
||||
|
||||
2. **Processing Errors**
|
||||
- Invalid data formats
|
||||
- AI processing failures
|
||||
- Resource limitations
|
||||
- Analysis timeouts
|
||||
|
||||
3. **Storage Errors**
|
||||
- Database connection issues
|
||||
- Transaction failures
|
||||
- Data validation errors
|
||||
- Concurrent access conflicts
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Data Collection**
|
||||
- Implement rate limiting
|
||||
- Use proper user agents
|
||||
- Handle redirects
|
||||
- Validate input data
|
||||
|
||||
2. **Content Processing**
|
||||
- Clean and normalize data
|
||||
- Handle encoding issues
|
||||
- Implement fallback strategies
|
||||
- Cache processed results
|
||||
|
||||
3. **Storage Management**
|
||||
- Use transactions
|
||||
- Implement data validation
|
||||
- Handle concurrent access
|
||||
- Maintain data integrity
|
||||
|
||||
## Future Enhancements
|
||||
|
||||
1. **Performance Optimizations**
|
||||
- Implement parallel processing
|
||||
- Add caching layer
|
||||
- Optimize database queries
|
||||
- Enhance error recovery
|
||||
|
||||
2. **Feature Additions**
|
||||
- Content performance tracking
|
||||
- Automated content planning
|
||||
- Enhanced competitive intelligence
|
||||
- Advanced topic clustering
|
||||
|
||||
3. **Integration Improvements**
|
||||
- API endpoints
|
||||
- Export capabilities
|
||||
- Data visualization
|
||||
- Progress tracking
|
||||
|
||||
4. **UI/UX Enhancements**
|
||||
- Interactive visualizations
|
||||
- Real-time progress updates
|
||||
- Export interfaces
|
||||
- Customization options
|
||||
|
||||
## Contributing
|
||||
|
||||
When contributing to these utility modules:
|
||||
|
||||
1. Follow the existing code structure
|
||||
2. Add comprehensive error handling
|
||||
3. Include unit tests
|
||||
4. Update documentation
|
||||
5. Follow PEP 8 style guide
|
||||
|
||||
## Dependencies
|
||||
|
||||
- BeautifulSoup4: HTML parsing
|
||||
- NLTK: Natural language processing
|
||||
- SQLAlchemy: Database operations
|
||||
- Streamlit: UI components
|
||||
- Requests: HTTP requests
|
||||
|
||||
## License
|
||||
|
||||
This project is licensed under the MIT License - see the LICENSE file for details.
|
||||
Reference in New Issue
Block a user