NEW: Facebook Comments API - Extract Comments from Posts & Reels

We're excited to announce a major expansion to the Scrape Creators API: you can now extract comments from Facebook posts and reels!

This powerful new endpoint opens up entirely new possibilities for social media research, sentiment analysis, and engagement tracking on the world's largest social platform.

Introducing Facebook Comments Extraction

The new Facebook comments endpoint allows you to programmatically access comment data from any public Facebook post or reel, providing deep insights into audience engagement and community sentiment that was previously difficult to obtain at scale.

API Endpoint

GET /v1/facebook/post/comments

This simple endpoint takes a Facebook post or reel URL and returns comprehensive comment data, making it easy to integrate Facebook comment analysis into your existing workflows.

What Data You Get

The Facebook comments API provides rich comment data including:

• Comment Content: Full text of user comments and replies
• User Information: Commenter names and profile links (where publicly available)
• Engagement Metrics: Like counts, reply counts, and reaction data for individual comments
• Timestamps: When comments were posted for temporal analysis
• Thread Structure: Parent-child relationships for comment replies
• Reaction Types: Specific Facebook reaction types (like, love, angry, etc.)

This comprehensive data set enables sophisticated analysis of how audiences engage with content beyond simple like and share metrics.

Why Facebook Comments Matter

Understanding True Engagement

While likes and shares provide surface-level engagement metrics, comments reveal the depth of audience connection with content. Comments show:

• Genuine Interest: People who comment are typically more invested than those who simply react
• Sentiment Analysis: Comments provide rich text data for understanding audience opinions
• Community Building: Comment threads reveal how content sparks conversations and builds communities
• Content Performance: Comment quality and quantity often correlate with content virality

Business Intelligence Applications

For businesses and marketers, Facebook comment data unlocks powerful insights:

• Brand Sentiment Monitoring: Track what people actually say about your brand, not just whether they like posts
• Competitor Analysis: Understand how audiences respond to competitor content and messaging
• Content Optimization: Identify which types of posts generate meaningful conversations
• Crisis Management: Monitor comment sentiment for early warning signs of reputation issues

Real-World Use Cases

Social Media Marketing Agencies

Marketing agencies can now provide clients with deeper engagement analysis:

• Campaign Performance: Move beyond vanity metrics to understand actual audience sentiment
• Content Strategy: Identify topics and formats that generate meaningful discussions
• Influencer Vetting: Analyze comment quality on influencer posts to assess audience authenticity
• Competitive Intelligence: Monitor competitor engagement patterns and audience feedback

Market Research

Researchers gain access to authentic consumer opinions:

• Product Feedback: Analyze comments on product launches and announcements
• Brand Perception Studies: Understand how audiences discuss brands in natural conversation
• Trend Analysis: Identify emerging topics and sentiment shifts in real-time
• Consumer Behavior: Study how different demographics engage with various content types

Customer Service and Support

Customer service teams can monitor and respond more effectively:

• Issue Detection: Identify customer complaints and concerns mentioned in comments
• Response Optimization: Analyze which types of responses generate positive community reactions
• FAQ Development: Use common comment questions to improve support documentation
• Community Management: Understand conversation patterns to guide engagement strategies

Technical Implementation

Getting Started

The Facebook comments API follows the same simple pattern as other ScrapeCreators endpoints:

 import requests  # Extract comments from a Facebook post response = requests.get(  'https://api.scrapecreators.com/v1/facebook/post/comments',  params={'url': 'https://facebook.com/post-url'} )  comments_data = response.json()

Processing Comment Data

The API returns structured comment data that's easy to process:

 // Example response structure {  "comments": [  {  "id": "comment_id",  "text": "This is amazing! Thanks for sharing",  "author": {  "name": "John Doe",  "profile_url": "https://facebook.com/johndoe"  },  "timestamp": "2025-09-15T10:30:00Z",  "likes": 15,  "replies": 3,  "reactions": {  "like": 10,  "love": 3,  "haha": 1,  "wow": 1  }  }  ],  "total_comments": 247,  "post_url": "https://facebook.com/original-post" }

Batch Processing

For large-scale analysis, implement batch processing workflows:

 post_urls = [  "https://facebook.com/post1",  "https://facebook.com/post2",  "https://facebook.com/post3" ]  all_comments = [] for url in post_urls:  response = requests.get('/v1/facebook/post/comments',  params={'url': url})  all_comments.extend(response.json()['comments'])  # Analyze combined comment data

Advanced Analysis Opportunities

Sentiment Analysis

Combine Facebook comment data with natural language processing:

 from textblob import TextBlob  def analyze_sentiment(comments):  sentiments = []  for comment in comments:  blob = TextBlob(comment['text'])  sentiments.append({  'comment': comment['text'],  'polarity': blob.sentiment.polarity,  'subjectivity': blob.sentiment.subjectivity  })  return sentiments  # Analyze sentiment of all comments sentiment_results = analyze_sentiment(comments_data['comments'])

Engagement Pattern Analysis

Identify what drives meaningful conversations:

 def analyze_engagement_patterns(comments):  high_engagement = [c for c in comments if c['likes'] > 10 or c['replies'] > 2]   # Identify common themes in high-engagement comments  common_words = analyze_word_frequency([c['text'] for c in high_engagement])   return {  'high_engagement_rate': len(high_engagement) / len(comments),  'common_themes': common_words[:10]  }

Temporal Analysis

Track how comment sentiment and volume change over time:

 import pandas as pd  def temporal_analysis(comments):  df = pd.DataFrame(comments)  df['timestamp'] = pd.to_datetime(df['timestamp'])   # Group by hour and analyze patterns  hourly_stats = df.groupby(df['timestamp'].dt.hour).agg({  'likes': 'mean',  'text': 'count'  }).rename(columns={'text': 'comment_count'})   return hourly_stats

Privacy and Ethical Considerations

Public Data Only

The Facebook comments API only accesses publicly available comment data. Private posts, restricted content, and user information requiring authentication are not accessible through this endpoint.

Respectful Usage

When using Facebook comment data:

• Respect User Privacy: Don't attempt to correlate comments with private user information
• Follow Platform Guidelines: Ensure your usage complies with Facebook's terms of service
• Data Protection: Handle user-generated content responsibly and in accordance with privacy regulations
• Ethical Analysis: Use comment data for legitimate business purposes, not harassment or stalking

Integration with Existing Workflows

Social Media Dashboards

Add Facebook comment analysis to your existing social media monitoring:

 def create_engagement_report(post_url):  # Get post metrics  post_data = get_facebook_post_data(post_url)   # Get comment data   comments = get_facebook_comments(post_url)   # Combine for comprehensive analysis  return {  'post_metrics': post_data,  'comment_analysis': analyze_comments(comments),  'overall_sentiment': calculate_sentiment(comments)  }

CRM Integration

Connect comment insights with customer relationship management:

 def update_customer_insights(customer_id, comments):  # Identify comments from known customers  customer_comments = find_customer_comments(comments, customer_id)   # Update CRM with sentiment and engagement data  crm_client.update_customer(customer_id, {  'social_engagement_score': calculate_engagement_score(customer_comments),  'sentiment_history': extract_sentiment_trends(customer_comments)  })

Performance and Scalability

Rate Limiting

The Facebook comments endpoint includes intelligent rate limiting to ensure reliable access:

• Reasonable Limits: Designed for real-world usage patterns without unnecessary restrictions
• Transparent Pricing: Pay-per-use model scales with your actual needs
• Batch Optimization: Efficient processing for large-scale comment extraction

Caching Strategy

Implement caching to optimize performance and costs:

 import redis import json  cache = redis.Redis()  def get_comments_cached(post_url):  cache_key = f"fb_comments:{post_url}"  cached = cache.get(cache_key)   if cached:  return json.loads(cached)   # Fetch fresh data  comments = fetch_facebook_comments(post_url)   # Cache for 1 hour  cache.setex(cache_key, 3600, json.dumps(comments))   return comments

Future Enhancements

We're continuously improving the Facebook comments API with planned features:

Coming Soon:

• Enhanced Filtering: Filter comments by sentiment, engagement level, or keywords
• Historical Analysis: Track comment sentiment changes over time for the same post
• User Analytics: Aggregate insights about frequent commenters and community leaders
• Multi-language Support: Improved handling of comments in different languages
• Real-time Monitoring: Webhook notifications for new comments on tracked posts

Getting Started Today

Immediate Access

The Facebook comments API is available now for all ScrapeCreators users. No special setup required – just start making requests to the new endpoint.

Testing the Feature

To test Facebook comment extraction:

1. Find a public Facebook post with active comments
2. Use the /v1/facebook/post/comments endpoint with the post URL
3. Analyze the returned comment data structure
4. Integrate comment analysis into your existing workflows

Documentation

Complete API documentation with request/response examples is available at docs.scrapecreators.com/v1/facebook/post/comments.