Add comprehensive keyword research and secondary keyword analysis fea…

[runzel]

…tures

Inspired by ALwrity's SEO capabilities, this commit adds advanced keyword research tools to ContentSwift for better content optimization.

Features Added:

• LSI (Latent Semantic Indexing) keyword extraction using TF-IDF
• Co-occurring keyword analysis with frequency tracking
• Question-based keyword identification from autocomplete
• Related keyword suggestions from Google SERP
• Keyword difficulty analysis based on competitor usage
• Optional keyword metrics integration (DataForSEO API)

Backend Changes:

• New KeywordResearchService class with comprehensive analysis methods
• Added /keyword-research/{post_id} endpoint for full keyword analysis
• Added /keyword-analyze/ endpoint for on-demand keyword metrics
• Updated Post model with keyword_research JSON field
• Added scikit-learn dependency for TF-IDF analysis
• Store full article content for better keyword extraction
• Created database migration script for schema update

Frontend Changes:

• New KeywordResearch component displaying all keyword types
• Added "Keywords" tab to Sidebar navigation
• Responsive UI with color-coded difficulty ratings
• Organized display of LSI, co-occurring, question, and related keywords
• Real-time loading states and error handling

Documentation:

• Comprehensive KEYWORD_RESEARCH_FEATURES.md documentation
• Updated README.md with new feature highlights
• Added .env.example with optional API credentials
• Detailed usage instructions and API examples

Technical Details:

• TF-IDF vectorization for semantic keyword analysis
• N-gram extraction (unigrams, bigrams, trigrams)
• Window-based co-occurrence detection (50-word window)
• Intent classification for question keywords
• Competitive density analysis for difficulty rating

All Python code syntax validated. Ready for testing and deployment.