Pinterest Comment Scraper (Flattened Bulk Export)

Pinterest Comment Scraper — Flattened Bulk Export

Pinterest Comment Scraper (Flattened Bulk Export) pulls every comment off a batch of Pinterest pins and flattens each one into dot-separated, CSV/Excel-ready columns — no post-processing needed before it lands in a spreadsheet. It's built for social listening and community teams monitoring comment activity across many pins at once, e-commerce and brand teams auditing customer feedback on product pins in bulk, and data teams who need a hard USD spend cap on a large export rather than an open-ended bill. No Pinterest login is required. Every section below documents an input, an output field, or exactly how the flattening, concurrency, and spend-cap mechanics work.

What is Pinterest Comment Scraper (Flattened Bulk Export)?

This Actor calls Pinterest's own internal UnifiedCommentsResource endpoint — the same one the Pinterest web app uses to load a pin's comments — for a batch of pins in parallel, and reshapes each comment into either a flat, spreadsheet-ready row or the original nested JSON, your choice.

Key capabilities, read from the source:

• True dot-notation flattening, not a partial one. _flatten() in src/main.py recursively walks every nested dict and list into parent.child / array.0.field keys, so a comment's user.username, images.0.url, and similar deep fields all become individual flat CSV columns with zero manual post-processing.
• Parallel pin processing with a real concurrency counter. maxConcurrency (1-20) controls how many pins are fetched simultaneously via an asyncio.Semaphore, and the run log reports the actual peak concurrency reached — not just the configured cap.
• A hard client-side USD spend cap, on top of Apify's own limit. ChargeTracker estimates cost at a documented $0.005 per charged row and stops the run cleanly once maxTotalChargeUsd would be exceeded — checked before pushing the row that would cross it, so the run never overshoots the cap by even one row, and this operates independently of (in addition to) Apify's own platform-level pay-per-event charge limit.
• Soft-block-aware proxy escalation. A response with no resource_response payload is treated as SoftBlockError — evidence of a soft block rather than a genuine empty result — triggering escalation through none → datacenter → residential proxy tiers with retries at each.
• Real image data, never synthetic dimensions. _build_image_obj() only reports a width/height when Pinterest's own API actually returned one for that image size; a size with no API data gets a real, pattern-derived pinimg.com URL but null dimensions rather than a fabricated number.
• 22-region domain support. domain selects the correct regional Pinterest domain (e.g. jp.pinterest.com, uk.pinterest.com) so comment fetching matches where the pin actually lives.

What data can I extract with Pinterest Comment Scraper (Flattened Bulk Export)?

Every field below is read directly from _transform() and _normalize_user() in src/main.py — shown here in nested form; with flattenedOutput on (the default), every nested key becomes a dot-separated column instead.

Field	Example Value	Use Case
pinUrl	https://www.pinterest.com/pin/1618549864585211/	Which pin this comment belongs to
id / node_id	comment IDs	Unique identifiers
type	"comment", "aggregatedcomment", or "userdiditdata"	Pinterest's own comment-type classification
details	comment text	The comment body
done_at	ISO timestamp	When the comment was posted
like_count / helpful_count	12 / 3	Reaction counts, read from reaction_counts
comment_count	2	Replies on this comment, when applicable
tags	array	Any tags Pinterest attaches to the comment
videos	array	Video attachments, when present
user.username / user.full_name / user.node_id / user.id	commenter identity	Who wrote the comment
user.is_private_profile	false	Whether the commenter's profile is private
user.image_medium_url	avatar URL	Falls back to Pinterest's own default avatar image when the commenter has none
images.<n>.url / images.<n>.width / images.<n>.height	real pinimg.com URLs / real dimensions or null	Attached images, at 3 sizes each (originals, 550x, 150x150)
marked_helpful_by_me / liked_by_me	false / false	Session-relative flags, always false for a logged-out scrape

Why not build this yourself?

Pinterest has no public API for pin comments — the only path is its internal UnifiedCommentsResource endpoint, which requires a working CSRF token seeded from a real pin-page visit and a resolved aggregated_pin_id fetched via a separate internal PinResource call, since Pinterest no longer embeds that ID directly in the logged-out pin HTML. On top of that, a soft block on this endpoint returns a technically-valid JSON response with no resource_response payload rather than a clear error status, so a naive scraper can silently produce empty results instead of retrying. This Actor already handles the two-step ID resolution, soft-block detection, and proxy escalation, plus the flattening work needed to make deeply nested comment JSON usable in a spreadsheet without any manual column engineering.

How to use data extracted from Pinterest Comment Scraper (Flattened Bulk Export)?

Social listening and community management

Run a batch of your brand's pins with flattenedOutput on and load the export directly into a spreadsheet or BI tool for sentiment review — no column restructuring needed since every field is already flat.

E-commerce and product feedback auditing

Scrape comments across a catalog of product pins in one run, using maxConcurrency to process many pins in parallel, and filter by like_count/helpful_count to surface the most-engaged customer feedback first.

Large-scale exports on a budget

Set maxTotalChargeUsd to your budget ceiling before running a very large pin batch — the run stops cleanly once that estimated spend is reached, rather than running to completion and surprising you with the bill.

AI agents and data pipelines

Because flattened rows have no nested structure, an agent or ETL pipeline can load them directly into a relational table or DataFrame without a separate JSON-normalization step.

🔼 Input sample

Parameter	Required	Type	Description	Example Value
urls	Yes	array	Pinterest pin links or numeric IDs, one per line.	["https://www.pinterest.com/pin/1618549864585211/"]
domain	No	string enum	Regional Pinterest domain matching where the pins live (22 regions). Default "www.pinterest.com".	"uk.pinterest.com"
limit	No	integer	Comments to fetch per pin. Default 10.	100
flattenedOutput	No	boolean	Flatten to dot-separated spreadsheet columns. false keeps nested JSON. Default true.	true
maxConcurrency	No	integer (1–20)	Pins processed in parallel. Default 10.	15
maxTotalChargeUsd	No	number	Hard spend cap in USD, estimated at $0.005/row. Default 10.	25
proxyConfiguration	No	object	Optional; the Actor auto-escalates none → datacenter → residential on a block.	{"useApifyProxy": false}

{
  "urls": ["https://www.pinterest.com/pin/1618549864585211/"],
  "limit": 100,
  "flattenedOutput": true,
  "maxConcurrency": 10,
  "maxTotalChargeUsd": 25
}

Common pitfall: maxTotalChargeUsd's $0.005-per-row figure is an explicitly documented client-side estimate used only to stop the run early — the authoritative billing cap is still Apify's own pay-per-event charge limit, so don't treat the estimated total as your exact final bill.

🔽 Output sample

Output is one JSON row per comment, pushed to the run's default dataset and charged as one row_result event per row (unless stopped early by maxTotalChargeUsd).

{
  "pinUrl": "https://www.pinterest.com/pin/1618549864585211/",
  "id": "987654321",
  "type": "comment",
  "details": "Love this idea, saving it!",
  "done_at": "2026-07-20T14:32:00",
  "like_count": 12,
  "helpful_count": 3,
  "user.username": "examplecreator",
  "user.full_name": "Example Creator",
  "user.node_id": "1122334455",
  "user.is_private_profile": false,
  "images.0.url": "https://i.pinimg.com/originals/de/25/8d/de258d5c5e1577b30c8744148baa2fc9.jpg",
  "images.0.width": 736,
  "images.0.height": 1104
}

With flattenedOutput: false, the same row keeps user and images as nested objects/arrays instead.

How do you filter and target specific comments?

Volume vs. spend control. limit caps depth per pin, while maxTotalChargeUsd caps total run spend across every pin combined — use a lower limit for a broad, shallow sentiment scan across many pins, or a high limit on a small set of pins when you need full comment threads.

Region matters for reachability. domain should match where the pin actually lives — a mismatched region can affect which comments and images resolve correctly, since Pinterest serves regional variants of its endpoints.

Flattened vs. nested is a workflow choice, not a data-completeness one. Both modes return exactly the same underlying data; flattenedOutput: false is only useful if your downstream tool already handles nested JSON natively and you'd rather keep the original structure.

Three real examples:

{ "urls": ["https://www.pinterest.com/pin/1618549864585211/"], "limit": 500, "maxConcurrency": 1 }

Deep single-pin comment thread export.

{ "urls": ["pin1", "pin2", "pin3", "pin4", "pin5"], "limit": 20, "maxConcurrency": 20, "maxTotalChargeUsd": 5 }

Fast, budget-capped shallow scan across 5 pins.

{ "urls": ["https://uk.pinterest.com/pin/123456789/"], "domain": "uk.pinterest.com", "limit": 50 }

Region-matched single-pin export for a UK-hosted pin.

▶️ Want to try other scrapers?

Scraper	What it extracts
Pinterest Creator Board Discovery Scraper	Creator board listings and metadata
Reddit Posts Scraper with Author Media Details	Post threads with author and media data
Instagram Comment Engagement Scraper	Per-post comment threads and commenter identity
TikTok Comments Scraper — Full Reply Threads	Nested TikTok comment threads

How to extract Pinterest comments programmatically

This Actor runs as a standard Apify Actor call — one API call in, structured JSON dataset out, using your Apify API token.

Python example

from apify_client import ApifyClient

client = ApifyClient("<YOUR_API_TOKEN>")
run = client.actor("<YOUR_USERNAME>/pinterest-comment-scraper-flattened-bulk-export").call(run_input={
    "urls": ["https://www.pinterest.com/pin/1618549864585211/"],
    "limit": 100,
    "maxTotalChargeUsd": 20,
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["user.username"], item["details"])

Export to spreadsheets or CRM

With flattenedOutput: true (the default), the Apify Console's CSV/Excel export needs zero post-processing — every nested field is already its own column, ready to drop into a spreadsheet or BI tool.

Is it legal to scrape Pinterest comments?

Scraping publicly visible Pinterest pins and their comments is generally lawful, since this data is published for anyone to view without logging in — the underlying legal question was tested directly in hiQ Labs v. LinkedIn (9th Cir.), which held that scraping public, non-password-protected data does not violate the U.S. Computer Fraud and Abuse Act, and the same reasoning has been applied broadly across public social platforms. Commenter usernames and full names are personal data under GDPR/CCPA when tied to an identifiable individual, so treat that subset accordingly if you store or reuse it at scale, and consult legal counsel for commercial applications.

❓ FAQ

What's the actual per-row cost, and is $0.005 exact?

$0.005 per row is an explicitly documented estimate this Actor uses only to decide when to stop the run early under maxTotalChargeUsd — your actual billed amount is governed by Apify's own pay-per-event pricing and charge limit, which operates independently on top of this client-side estimate.

Does flattenedOutput change what data I get?

No — it only changes the shape. true (default) produces dot-separated flat columns (user.username, images.0.url); false keeps the same data as nested JSON objects and arrays. Every field is present either way.

What happens if a pin's comments can't be fetched?

The Actor escalates through a none → datacenter → residential proxy ladder with retries at each tier before giving up on that pin; a pin that ultimately fails is listed in the run's summary log rather than silently producing an empty, unexplained result.

Why are some image dimensions null?

width/height are only populated when Pinterest's own API response for that image size actually included them — a size the API didn't return dimensions for gets a real, correctly-formed image URL but null dimensions rather than a guessed number.

Do I need a Pinterest account to use this?

No — it's built to run without a login, using a fresh CSRF token seeded per pin rather than an authenticated session.

Can I process many pins at once without a huge bill?

Yes — set maxConcurrency high for speed and maxTotalChargeUsd to your budget ceiling; the run stops cleanly the moment the estimated spend would exceed that cap, rather than running every pin to completion first.

Does this work with AI agent frameworks?

Yes — call it as a standard HTTP endpoint via the Apify API from any agent framework capable of making an API call; there's no MCP-specific integration for this Actor.

Conclusion

Pinterest Comment Scraper (Flattened Bulk Export) turns a batch of pins into spreadsheet-ready comment data in one run — flat columns with zero post-processing, real (never fabricated) image dimensions, and a hard spend cap that keeps a large export from running away on cost. It fits social listening, product-feedback auditing, and any bulk-export workflow that needs a budget ceiling built in. Start a run from the Apify Console or the Apify API with your target pin URLs to get your first flattened comment export.