> ## 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 Comments By Person

> Fetch LinkedIn comments from a person's profile using LinkedIn RapidAPI, including engagement metrics and comment content.

## What It Does

* **Fetch LinkedIn Comments**: Retrieves comments from a person's LinkedIn profile using LinkedIn RapidAPI.
* **Engagement Metrics**: Provides detailed engagement data including the number of likes, comments, empathy reactions, and more.
* **Comment Content**: Fetches the actual comment text, along with metadata such as author and timestamp.
* **Batch Processing**: Supports batch processing via template variables to process multiple profiles at once.
* **Pagination Handling**: Automatically handles pagination for up to 250 comments.
* **Metadata**: Includes metadata such as the total number of reactions and whether a post was reshared.

***

## 🏁 Getting Started

<Frame>
  <img src="https://mintcdn.com/nurturev/OitEIaKlfl7lCKCJ/images/Get%20Comments%20By%20Person%20Node%20config%20screenshot.png?fit=max&auto=format&n=OitEIaKlfl7lCKCJ&q=85&s=f18ec0bdca2634fb0b6c54f7c5c45395" alt="Get Comments By Person Node config screenshot" style={{ borderRadius: '0.5rem', width: '100%', margin: '1.5rem 0' }} width="1178" height="1664" data-path="images/Get Comments By Person Node config screenshot.png" />
</Frame>

<Steps>
  <Step title="Add the Get Comments By Person Node">Drag and drop the node into your workflow to fetch LinkedIn comments.</Step>
  <Step title="Provide LinkedIn Profile URL">Add the LinkedIn profile URL and define the limit for comments to fetch.</Step>
  <Step title="Run the Workflow">Execute the workflow to fetch LinkedIn comments based on the provided settings.</Step>
  <Step title="Review the Output">The output will include the comments, engagement metrics, and associated metadata in the DataFrame.</Step>
</Steps>

***

## Inputs

| Input Name        | Type            | Required                            | Description                                                                                                         |
| ----------------- | --------------- | ----------------------------------- | ------------------------------------------------------------------------------------------------------------------- |
| `input_df_s3_url` | `Optional[str]` | Yes, if template variables are used | S3 URL to an input DataFrame (CSV/Parquet). Required when using template variables in settings for batch processing |

***

## Outputs

The node returns a **List\[Dict\[str, Any]]** where each dictionary contains:

| Output Name         | Type   | Description                                                                          |
| ------------------- | ------ | ------------------------------------------------------------------------------------ |
| `s3_output_url`     | `str`  | S3 URL of the output DataFrame (Parquet format)                                      |
| `s3_output_url_csv` | `str`  | S3 URL of the output DataFrame (CSV format)                                          |
| `file_info`         | `Dict` | Contains metadata: `rows_count` (int), `columns_count` (int), `columns` (List\[str]) |
| `handle_condition`  | `str`  | Always returns `"default"` for this node                                             |

### Output DataFrame Structure

The output DataFrame will contain the following LinkedIn comment fields:

* `person_comments`: Boolean indicating if the comment is highlighted (mapped from `highlighted_comments`)
* `urn`: LinkedIn URN identifier for the comment
* `posted`: Timestamp when the comment was posted
* `post_author`: LinkedIn URL of the comment author (mapped from `poster_linkedin_url`)
* `poster`: Author information object containing name and other details
* `post_url`: URL of the original post where the comment was made
* `text`: The actual comment text content
* `num_appreciations`: Number of appreciations on the comment
* `num_comments`: Number of replies to this comment
* `num_empathy`: Number of empathy reactions
* `num_entertainments`: Number of entertainment reactions
* `num_interests`: Number of interest reactions
* `num_likes`: Number of likes on the comment
* `num_praises`: Number of praise reactions
* `num_reactions`: Total number of reactions
* `num_reposts`: Number of reposts
* `reshared`: Boolean indicating if the post was reshared
* `resharer_comment`: Comment text if the post was reshared

**Note:** When input data is provided, all original columns are preserved and merged with the LinkedIn comment fields. Column name conflicts are resolved automatically using the column name resolution strategy.

***

## How It Works

1. **Data Loading**: Loads input data if provided using `data_loading_helper`.
2. **Processing**:
   * Single or batch processing mode based on input.
   * Template variables in LinkedIn URL are resolved and processed.
3. **API Interaction**: Calls LinkedIn RapidAPI for comment data fetching.
4. **Pagination**: Handles pagination automatically based on the specified limit (up to 250 comments).
5. **Credit Management**: Tracks credit consumption per page (5 credits per page).
6. **Output Generation**:
   * Merges LinkedIn comment data with original input columns.
   * Saves results in both Parquet and CSV formats to S3.

***

## 🚀 Example Use Cases & Prompts

| Use Case                  | Setup or Prompt Example                             |
| ------------------------- | --------------------------------------------------- |
| **Social Media Analysis** | Track engagement metrics for comments on a profile  |
| **Competitor Research**   | Fetch and analyze comments on competitor's posts    |
| **Influencer Evaluation** | Analyze comments to gauge the influence of a person |
| **Sentiment Analysis**    | Assess sentiment through comments and reactions     |

***

## ✨ Pro Tips

<Tip>
  Use **template variables** in `linkedin_url` to process multiple LinkedIn profiles in batch mode.
</Tip>

<Tip>
  Keep an eye on **API rate limits** to avoid hitting credit limits. Consider limiting the number of comments fetched.
</Tip>

***

## ⚠️ Important Considerations

<Warning>
  Requires **LinkedIn RapidAPI credentials** for authentication and data access.
</Warning>

<Warning>
  Comments are fetched based on the limit setting (max 250 comments). If the limit exceeds 250, only the first 250 comments will be returned.
</Warning>

***

## 🛠 Troubleshooting & Gotchas

| Symptom                    | Likely Cause                        | Quick Fix                                                      |
| -------------------------- | ----------------------------------- | -------------------------------------------------------------- |
| **API Rate Limit Reached** | Too many requests in a short period | Wait for the rate limit reset or reduce the number of requests |
| **Invalid LinkedIn URL**   | Malformed or incorrect URL          | Double-check the LinkedIn URL format and ensure it’s valid     |
| **Missing Comments**       | Insufficient public comments        | Check if the LinkedIn profile has public comments available    |

***

## 📝 FAQ

<AccordionGroup>
  <Accordion title="What LinkedIn API vendor is supported?">
    Currently, only the **`linkedin_rapid_api`** vendor is supported for fetching LinkedIn comments.
  </Accordion>

  <Accordion title="Can I fetch comments for multiple profiles?">
    Yes, by using **template variables** in the LinkedIn URL setting, you can fetch comments for multiple profiles in batch mode.
  </Accordion>
</AccordionGroup>

***

## 💰 Pricing

The **Get Comments By Person Node** incurs **5 credits** per page fetched from LinkedIn RapidAPI.

| Action            | Credit Cost        |
| ----------------- | ------------------ |
| Fetching comments | 5 credits per page |

<Note>
  Credits are consumed based on the number of pages fetched. A single page can return up to 50 comments.
</Note>

***

<p style={{ fontSize: '1rem', fontWeight: 'bold', marginTop: '1.5rem' }}>
  Start scraping LinkedIn comments and track engagement effortlessly! 🚀
</p>