Reddit Comment Sentiment Analyzer

Scrape Reddit comments and classify their sentiment as positive, negative, or neutral. Extracts comment text, scores, authors, nesting depth, and applies keyword-based sentiment analysis. Uses old.reddit.com for reliable cheerio-based HTML parsing.

Features

• Extracts all comments from Reddit threads including nested replies
• Classifies each comment as positive, negative, or neutral using keyword analysis
• Provides sentiment scores showing positive and negative word counts
• Supports comment depth limiting to focus on top-level discussions
• Configurable sorting (top, new, controversial, etc.)
• Automatic URL conversion from www.reddit.com to old.reddit.com
• Includes thread metadata (title, author, score, subreddit) for context
• Handles both HTML parsing and JSON API responses

Input Parameters

Parameter	Type	Default	Description
urls	array	required	List of Reddit thread URLs to scrape comments from
maxResults	integer	100	Maximum number of comments to extract per thread (0 = unlimited)
maxDepth	integer	-1	Max nesting depth of replies (-1 = unlimited, 0 = top-level only)
sortBy	string	"confidence"	Comment sort order: confidence, top, new, controversial, old, qa
includePostData	boolean	true	Include original post title, author, and score with each comment
useResidentialProxy	boolean	false	Enable residential proxies for better success rates

Output Fields

Field	Type	Description
author	string	Reddit username of the commenter
commentText	string	Full text content of the comment
score	number	Comment score (upvotes minus downvotes)
depth	number	Nesting depth (0 = top-level comment)
postedDate	string	ISO timestamp of when the comment was posted
sentiment	string	Classified sentiment: positive, negative, or neutral
positiveScore	number	Count of positive sentiment keywords found
negativeScore	number	Count of negative sentiment keywords found
commentId	string	Reddit's internal comment identifier
parentId	string	Parent comment/post identifier
permalink	string	Direct URL to the comment
threadTitle	string	Title of the Reddit thread (if includePostData is true)
threadAuthor	string	Author of the thread (if includePostData is true)
threadScore	number	Score of the thread (if includePostData is true)
subreddit	string	Subreddit name (if includePostData is true)

Example Output

{
  "author": "tech_enthusiast",
  "commentText": "This is an amazing breakthrough in AI research. The results are incredibly impressive.",
  "score": 245,
  "depth": 0,
  "postedDate": "2024-01-15T14:30:00.000Z",
  "sentiment": "positive",
  "positiveScore": 3,
  "negativeScore": 0,
  "commentId": "t1_abc123",
  "parentId": "t3_xyz789",
  "permalink": "https://old.reddit.com/r/technology/comments/xyz789/post_title/abc123/",
  "threadTitle": "New AI Model Achieves Record Performance",
  "threadAuthor": "science_reporter",
  "threadScore": 1500,
  "subreddit": "technology"
}

Use Cases

• Brand Monitoring: Track sentiment around your brand or product in Reddit discussions
• Market Research: Analyze public opinion on products, services, or industry trends
• Community Analysis: Understand the tone and sentiment of specific subreddit communities
• Product Feedback: Gather and classify user feedback from Reddit product discussions
• Content Research: Identify positive and negative talking points on any topic
• Crisis Monitoring: Detect negative sentiment spikes in real-time discussions

Sentiment Classification

The classifier uses curated keyword lists:

• Positive: Words like "amazing", "great", "love", "helpful", "recommend", "brilliant"
• Negative: Words like "terrible", "hate", "awful", "broken", "disappointing", "scam"
• Neutral: Comments without strong positive or negative keywords, or with balanced sentiment

Cost Estimate

Using the default configuration (100 comments per thread):

• Estimated compute units: ~0.03 CU per run
• Cost per result: ~$0.00075 per comment (Mid tier)
• Average runtime: 15-30 seconds per thread
• Residential proxy usage increases costs by approximately 3-5x