Reddit Comments Scraper — Extract Discussion Trees, Replies & Sentiment

Extract complete Reddit comment threads, nested reply trees, author karma, upvotes, and AI sentiment analysis from any Reddit post URL or Post ID. Download clean, structured JSON/CSV records for market research, sentiment analysis, customer feedback discovery, and competitive intelligence.

Overview

Reddit Comments Scraper is a purpose-built data extraction tool designed specifically to collect public discussion comments and nested reply hierarchies from Reddit. Simply provide one or more Reddit post links or post IDs, and the actor will extract all discussion comments with full conversation context.

Each record includes comment body text, author username, upvote score, nesting depth, timestamp, canonical URL, parent post details (title, post author, post score, subreddit), and optional AI sentiment scoring (positive, negative, neutral, mixed, uncertain).

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Why Use Reddit Comments Scraper

• Customer Feedback & Product Review Mining: Discover real user complaints, unsolicited opinions, and feature requests directly from product-related Reddit threads.
• Brand Sentiment Analysis: Monitor brand perception and opinion trends across discussions with built-in AI sentiment analysis.
• Q&A & Community Knowledge Gathering: Extract expert answers, tutorials, and community advice from educational and support subreddits (e.g. r/AskReddit, r/learnprogramming).
• Competitive Intelligence: Analyze what users love or hate about competitor offerings in open discussions.
• LLM Training & Discourse Datasets: Collect structured conversation trees and dialogue pairs for AI fine-tuning and natural language processing.

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Pricing & Plans (No Hidden Fees)

Transparent and predictable pricing with no extra proxy costs, no setup fees, and no hidden maintenance charges.

Tiered Pricing Structure

Tier / Discount Level	Price per 1,000 Comments	Effective Savings	Minimum Scrape
No Discount (Standard / Pay-As-You-Go)	$4.00 / 1,000 items	Standard Rate	1 item
🥉 Bronze Discount	$2.00 / 1,000 items	50% OFF	20 items
🥈 Silver Discount	$1.80 / 1,000 items	55% OFF	20 items
🥇 Gold Discount	$1.60 / 1,000 items	60% OFF	20 items

Plan Comparison

Feature	Free Tier	Subscriber / Paid Tier
Free Daily Allowance	20 comments / run (4 runs / day free)	Unlimited
Pricing	$4.00 / 1,000 results (or free allowance)	Down to $1.60 / 1,000 results
Additional Fees	$0.00 (No extra fees)	$0.00 (No extra fees)
Proxy / Bandwidth Costs	Included ($0.00)	Included ($0.00)
Full Nested Reply Trees	✅	✅
AI Sentiment Analysis	✅	✅
Parent Post Details Attached	✅	✅
Granular Filters	✅	✅
Run Summary Dashboard	✅	✅

Free users can extract up to 20 comments per run (4 runs/day) completely free. Upgrade for volume discounts down to $1.60 / 1,000 comments with zero hidden fees.

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How to Use — Step by Step

1. Provide Reddit Post URLs: Paste one or more Reddit post links into the Reddit Post URLs input field (e.g. https://www.reddit.com/r/technology/comments/...).
2. Choose Sort Order: Select your preferred comment sorting method (confidence for Best, top for most upvoted, new for latest, or controversial).
3. Configure Depth & Limits: Set Max Comments per Post and optional Max Nesting Depth (set to 0 for top-level only, or -1 for full tree).
4. Enable AI Sentiment (Optional): Toggle AI Sentiment Analysis to automatically classify comment tone.
5. Click Start: Download results in JSON, CSV, Excel, XML, or HTML table format.

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Input Parameters

Parameter	Type	Default	Description
urls	string[]	–	List of Reddit post URLs to scrape comments from.
postIds	string[]	–	Optional list of Reddit post IDs (e.g. 1hvoazn).
commentsSort	string	confidence	Comment ranking order: confidence (Best), top, new, controversial, old, qa.
maxCommentsPerPost	integer	100	Maximum comments to extract per post thread.
maxTotalComments	integer	500	Safety cap for total comment records across all posts.
depthLimit	integer	-1	Maximum reply nesting depth (-1 for unlimited, 0 for top-level only).
includePostMetadata	boolean	true	Include parent post title, post author, post score, and subreddit in each comment row.
sentiment_analysis	boolean	false	Adds sentiment_score, sentiment_confidence, and sentiment_label to each comment.
minScore	integer	–	Only keep comments with at least this upvote score.
textContains	string	–	Only keep comments containing this keyword.
excludeKeywords	string[]	–	Exclude comments containing any of these keywords.
authorContains	string	–	Only keep comments from authors matching this substring.
excludeAuthors	string[]	["AutoModerator"]	Exclude comments from these usernames.

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Example Output: Comment Record

{
    "kind": "comment",
    "id": "m5un6bj",
    "postId": "1hvoazn",
    "postUrl": "https://www.reddit.com/r/Baking/comments/1hvoazn/my_best_cheesecake_so_far/",
    "parentId": "t3_1hvoazn",
    "body": "This looks absolutely incredible! Can you share the full recipe?",
    "author": "BakingFanatic",
    "score": 76,
    "subreddit": "Baking",
    "created_utc": "2025-01-07T10:13:48.000Z",
    "url": "https://www.reddit.com/r/Baking/comments/1hvoazn/my_best_cheesecake_so_far/m5un6bj/",
    "permalink": "/r/Baking/comments/1hvoazn/my_best_cheesecake_so_far/m5un6bj/",
    "depth": 0,
    "is_submitter": false,
    "sentiment_score": 3,
    "sentiment_label": "positive",
    "sentiment_confidence": 0.91,
    "sentiment_score_normalized": 0.92,
    "post_title": "My best cheesecake so far",
    "post_author": "ClearlyBulky",
    "post_score": 3489,
    "post_num_comments": 43,
    "post_subreddit": "Baking",
    "post_created_utc": "2025-01-07T10:09:56.000Z"
}

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API Access

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("YOUR_ACTOR_ID").call(run_input={
    "urls": ["https://www.reddit.com/r/technology/comments/example/"],
    "commentsSort": "top",
    "maxCommentsPerPost": 100,
    "sentiment_analysis": True,
    "includePostMetadata": True,
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"[{item.get('sentiment_label')}] {item['author']}: {item['body'][:80]}...")

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Support

For help or feature requests, use the Issues tab on the actor page in Apify Console.