Blog Post Optimizer

Comprehensive content analysis toolkit for optimizing blog posts, articles, and web content. Analyzes headlines, SEO elements, content structure, readability, and generates actionable recommendations with scores.

Quick Start from scripts.blog_post_optimizer import BlogPostOptimizer

Initialize optimizer

optimizer = BlogPostOptimizer()

Analyze a blog post

with open('blog_post.md', 'r') as f: content = f.read()

Full analysis

results = optimizer.analyze_full( content=content, headline="10 Ways to Boost Your Productivity", keywords=["productivity", "time management", "efficiency"] )

View scores

print(f"Overall Score: {results['overall_score']}/100") print(f"Headline Score: {results['headline']['score']}/100") print(f"SEO Score: {results['seo']['score']}/100") print(f"Readability Grade: {results['readability']['grade_level']}")

Export HTML report

optimizer.export_html_report(results, 'report.html')

Features 1. Headline Analysis Power Words Detection - Identifies emotional trigger words Character Count - Optimal range 50-60 characters Emotional Impact - Measures headline engagement potential A/B Suggestions - Generate alternative headlines 2. SEO Optimization Keyword Density - Target 1-2% for primary keyword Keyword Prominence - Check placement in first 100 words Meta Description - Auto-generate 150-160 characters Title Tag - Optimize for 50-60 characters URL Slug - Generate SEO-friendly slugs 3. Content Structure Heading Hierarchy - Validate H1/H2/H3 structure Paragraph Length - Ideal 3-5 sentences List Usage - Detect numbered and bulleted lists Image Placement - Check for visual elements Subheading Distribution - Ensure consistent spacing 4. Readability Analysis Flesch-Kincaid Grade - Target grade 8-10 Reading Ease - 60-70 is ideal Sentence Complexity - Average words per sentence Passive Voice - Percentage (aim for <10%) 5. Content Statistics Word Count - Article length tracking Reading Time - Estimated at 265 words/minute Character Count - Total characters Average Sentence Length - Words per sentence 6. Meta Tag Generation Open Graph - Social media preview tags Twitter Cards - Twitter-specific meta tags Schema.org - Article structured data (JSON-LD) API Reference BlogPostOptimizer optimizer = BlogPostOptimizer()

analyze_headline(headline: str) -> Dict

Analyze headline effectiveness.

Returns:

{ 'score': 75, # 0-100 'character_count': 52, 'power_words': ['boost', 'proven'], 'emotional_impact': 68, 'suggestions': [ "10 Proven Ways to Boost Your Productivity Today", "Boost Your Productivity: 10 Essential Strategies" ] }

analyze_seo(content: str, keywords: List[str]) -> Dict

Analyze SEO elements.

Returns:

{ 'score': 80, 'keyword_density': {'productivity': 1.8, 'time management': 0.9}, 'keyword_prominence': True, # In first 100 words 'meta_description': 'Discover 10 proven ways to boost productivity...', 'title_tag': '10 Ways to Boost Productivity | Your Site', 'url_slug': 'boost-productivity-10-ways' }

analyze_structure(content: str) -> Dict

Analyze content structure.

Returns:

{ 'score': 85, 'h1_count': 1, 'h2_count': 10, 'avg_paragraph_length': 4.2, # Sentences 'list_count': 3, 'warnings': ['Paragraph on line 45 is too long (8 sentences)'] }

analyze_readability(content: str) -> Dict

Calculate readability scores.

Returns:

{ 'flesch_kincaid_grade': 8.5, 'reading_ease': 65.2, 'avg_sentence_length': 15.3, 'passive_voice_pct': 8.5, 'complexity_score': 72 }

calculate_content_stats(content: str) -> Dict

Calculate basic content statistics.

Returns:

{ 'word_count': 1250, 'reading_time_minutes': 5, 'character_count': 7890, 'sentence_count': 85, 'paragraph_count': 28 }

generate_meta_tags(title: str, description: str, keywords: List[str]) -> Dict

Generate social media and SEO meta tags.

Returns:

{ 'open_graph': { 'og:title': '10 Ways to Boost Your Productivity', 'og:description': 'Discover proven strategies...', 'og:type': 'article' }, 'twitter_card': { 'twitter:card': 'summary_large_image', 'twitter:title': '10 Ways to Boost Your Productivity' }, 'schema_org': { '@context': 'https://schema.org', '@type': 'Article', 'headline': '10 Ways to Boost Your Productivity' } }

analyze_full(content: str, headline: str, keywords: List[str]) -> Dict

Complete analysis combining all methods.

Returns:

{ 'overall_score': 78, 'headline': {...}, 'seo': {...}, 'structure': {...}, 'readability': {...}, 'stats': {...}, 'recommendations': [ {'priority': 'high', 'issue': '...', 'fix': '...'} ] }

export_html_report(results: Dict, output_path: str)

Generate color-coded HTML report with charts and recommendations.

CLI Usage Single Blog Post Analysis python scripts/blog_post_optimizer.py \ --input blog_post.md \ --headline "10 Ways to Boost Your Productivity" \ --keywords "productivity,time management,efficiency" \ --output report.html \ --format html

Quick Analysis (JSON Output) python scripts/blog_post_optimizer.py \ --input article.txt \ --headline "Ultimate Guide to Python" \ --keywords "python,programming" \ --format json

Headline Analysis Only python scripts/blog_post_optimizer.py \ --headline-only "10 Productivity Hacks You Need to Know"

CLI Arguments Argument Description Default --input, -i Input file (txt/md/html) - --headline Blog post headline Extracted from content --keywords, -k Comma-separated keywords - --output, -o Output file path stdout --format, -f Output format (json/html) json --headline-only Analyze headline only False Examples Example 1: Full Analysis with HTML Report optimizer = BlogPostOptimizer()

with open('article.md') as f: content = f.read()

results = optimizer.analyze_full( content=content, headline="The Complete Guide to Remote Work", keywords=["remote work", "productivity", "work from home"] )

optimizer.export_html_report(results, 'seo_report.html')

Example 2: Headline Optimization optimizer = BlogPostOptimizer()

headline = "Ways to Improve Your Writing" analysis = optimizer.analyze_headline(headline)

print(f"Score: {analysis['score']}/100") print(f"Power words found: {', '.join(analysis['power_words'])}") print("\nSuggestions:") for suggestion in analysis['suggestions']: print(f" - {suggestion}")

Example 3: SEO Keyword Analysis optimizer = BlogPostOptimizer()

with open('post.md') as f: content = f.read()

seo = optimizer.analyze_seo( content=content, keywords=["python", "tutorial", "beginners"] )

for keyword, density in seo['keyword_density'].items(): print(f"{keyword}: {density:.1f}%")

Dependencies nltk>=3.8.0 textblob>=0.17.0 beautifulsoup4>=4.12.0 pandas>=2.0.0 matplotlib>=3.7.0 reportlab>=4.0.0 lxml>=4.9.0

Limitations HTML Parsing: Complex HTML structures may affect accuracy Keyword Context: Doesn't account for keyword context (positive/negative) Language: English only for readability and power words Power Words: Subjective; effectiveness varies by audience Readability: Formulas are guides, not absolute measures SEO: Search engine algorithms constantly change Image Analysis: Cannot analyze actual image content External Links: Doesn't validate external link quality