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What It Does

  • Pulls comments from one or multiple LinkedIn posts
  • Captures commenter details: name, headline, and LinkedIn profile URL
  • Records comment text and timestamp for engagement insights
  • Works in both single-post (trigger) mode and batch-processing mode

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🏁 Getting Started

Get Post Comments config screenshot
1

Add the node

Drag the Get Post Comments node into your workflow.
2

Enter a LinkedIn post URN

Paste a valid urn:li:activity:XXXXXX or insert from a column using @.
3

Set comment limit

Choose how many comments to fetch (1–250, default = 50).
4

Run in test mode (optional)

Use test mode during setup — it fetches only 1 page (50 comments) to save credits.
5

Connect downstream

Send enriched comments into analysis, scoring, or routing flows.

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Inputs

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🛠️ Required Fields

  • Post URN (✅)
    The LinkedIn post URN (urn:li:activity:1234567890).
    Why it matters: Defines which post’s comments will be fetched. Without this, nothing runs.
  • Limit (✅)
    Default: 50. Maximum number of comments to fetch per post (1–250).
    Why it matters: Controls how much engagement data you capture while managing credit use.

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Output

The node enriches your table with these columns, while preserving all existing columns:
  • commenter_name → Name of the person who made the comment
  • commenter_headline → Their LinkedIn headline/title
  • commenter_linkedin_url → Link to their LinkedIn profile
  • comment_time → Timestamp of when the comment was posted
  • comment → The actual comment text
Get Post Comments output screenshot
✨ If your dataset already has any of these column names, new ones are renamed automatically (e.g., comment_1, comment_2).

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How It Works

  1. Reads your selected post_urn (single or from a column).
  2. Fetches comments page by page until the limit is reached (max 250).
  3. Respects your chosen limit and test mode settings.
  4. Extracts commenter details and comment text.
  5. Appends new comment columns while preserving your input data.
  6. Handles errors gracefully — skips invalid URNs, logs issues, and continues.

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🚀 Example Use Cases & Prompts

Use CaseSetup Example
Campaign Engagement TrackingEnrich post URNs with all comments for sentiment analysis
Lead Prospecting via CommentsCapture commenter names + headlines to add to CRM
Event Promotion AnalysisCompare comment activity across multiple campaign posts
Sales Insight GatheringSurface comments from target accounts to guide outreach messaging

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✨ Pro Tips

  • Use test mode during setup to save credits while validating.
  • Rename output columns (e.g., comment_text, comment_author) for cleaner downstream use.
  • Batch multiple posts by passing a column of URNs — perfect for campaign reporting.

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⚠️ Important Considerations

  • Each page = 50 comments = 5 credits. Larger limits mean more pages and higher credit use.
  • Batch mode preserves all input rows — even if a post has no comments (comment fields stay blank).

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🛠 Troubleshooting & Gotchas

SymptomLikely CauseQuick Fix
No comments returnedPost has no comments or URN invalidDouble-check URN format (urn:li:activity:XXXX)
Output blank columnsTest mode enabledSwitch off test mode to fetch full limit
Flow stops mid-runCredits exhaustedRefill credits; check Slack notifications
Column renamed with _1Naming conflictUse custom output names for clarity

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📝 FAQ

Yes — connect a dataset with a column of post URNs and reference it with @column_name.
The row is preserved and comment fields remain blank.
Correct — test mode always fetches only 1 page (50 comments), even if your limit is higher.

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💰 Pricing

ActionCredit Cost
Fetch 1 page (50 comments)5 credits
Credits are consumed per page of 50 comments.

Add this node to your workflow to capture LinkedIn engagement context and fuel smarter RevOps insights. 🚀