> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nrev.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Get Post Comments

> Fetch comments from LinkedIn posts to enrich your workflows with commenter details and engagement context.

## 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

***

## 🏁 Getting Started

<Frame>
  <img src="https://mintcdn.com/nurturev/OitEIaKlfl7lCKCJ/images/Get%20Post%20Comments%20config%20screenshot.png?fit=max&auto=format&n=OitEIaKlfl7lCKCJ&q=85&s=c5211298e0859f5f1c19aed9438211d8" alt="Get Post Comments config screenshot" style={{ borderRadius: '0.5rem', width: '100%', margin: '1.5rem 0' }} width="1178" height="1664" data-path="images/Get Post Comments config screenshot.png" />
</Frame>

<Steps>
  <Step title="Add the node">Drag the **Get Post Comments** node into your workflow.</Step>
  <Step title="Enter a LinkedIn post URN">Paste a valid `urn:li:activity:XXXXXX` or insert from a column using `@`.</Step>
  <Step title="Set comment limit">Choose how many comments to fetch (1–250, default = 50).</Step>
  <Step title="Run in test mode (optional)">Use test mode during setup — it fetches only 1 page (50 comments) to save credits.</Step>
  <Step title="Connect downstream">Send enriched comments into analysis, scoring, or routing flows.</Step>
</Steps>

***

## Inputs

### 🛠️ 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.

***

## 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

<Note>
  ✨ If your dataset already has any of these column names, new ones are renamed automatically (e.g., `comment_1`, `comment_2`).
</Note>

***

## 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.

***

## 🚀 Example Use Cases & Prompts

| Use Case                      | Setup Example                                                     |
| ----------------------------- | ----------------------------------------------------------------- |
| Campaign Engagement Tracking  | Enrich post URNs with all comments for sentiment analysis         |
| Lead Prospecting via Comments | Capture commenter names + headlines to add to CRM                 |
| Event Promotion Analysis      | Compare comment activity across multiple campaign posts           |
| Sales Insight Gathering       | Surface comments from target accounts to guide outreach messaging |

***

## ✨ Pro Tips

<Tip>
  * 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.
</Tip>

***

## ⚠️ Important Considerations

<Warning>
  * 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).
</Warning>

***

## 🛠 Troubleshooting & Gotchas

| Symptom                  | Likely Cause                        | Quick Fix                                        |
| ------------------------ | ----------------------------------- | ------------------------------------------------ |
| No comments returned     | Post has no comments or URN invalid | Double-check URN format (`urn:li:activity:XXXX`) |
| Output blank columns     | Test mode enabled                   | Switch off test mode to fetch full limit         |
| Flow stops mid-run       | Credits exhausted                   | Refill credits; check Slack notifications        |
| Column renamed with `_1` | Naming conflict                     | Use custom output names for clarity              |

***

## 📝 FAQ

<AccordionGroup>
  <Accordion title="Can I fetch comments for multiple posts at once?">
    Yes — connect a dataset with a column of post URNs and reference it with `@column_name`.
  </Accordion>

  <Accordion title="What happens if a post has no comments?">
    The row is preserved and comment fields remain blank.
  </Accordion>

  <Accordion title="Does test mode really ignore the limit?">
    Correct — test mode always fetches only 1 page (50 comments), even if your limit is higher.
  </Accordion>
</AccordionGroup>

***

## 💰 Pricing

| Action                     | Credit Cost |
| -------------------------- | ----------- |
| Fetch 1 page (50 comments) | 5 credits   |

<Note>
  Credits are consumed per **page of 50 comments**.
</Note>

***

<p style={{ fontSize: '1rem', fontWeight: 'bold', marginTop: '1.5rem' }}>
  Add this node to your workflow to capture LinkedIn engagement context and fuel smarter RevOps insights. 🚀
</p>