LinkedIn to Google Sheets, comments sorted for you
You find a great LinkedIn post. The comments are gold. Then you waste the next hour scrolling, copying names, pasting reactions, and trying not to lose your place. It’s messy, and honestly it’s easy to miss the best insights.
Marketing managers feel it when they’re doing audience research. Agency owners hit the same wall when clients ask for “what people are saying.” And if you run a small business, LinkedIn Sheets automation is the difference between “we should track engagement” and actually tracking it.
This workflow pulls LinkedIn post comments and reactions into Google Sheets and sorts them into clean tabs. You’ll see what it automates, what results you can expect, and what you need to get it running.
How This Automation Works
The full n8n workflow, from trigger to final output:
n8n Workflow Template: LinkedIn to Google Sheets, comments sorted for you
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The Problem: LinkedIn Engagement Data Is Painful to Capture
LinkedIn is great at showing you engagement, and terrible at letting you do anything useful with it. If you want to tag themes, build a swipe file of objections, identify active commenters, or report on reaction trends, you end up doing it manually. One tab open for the post, one for your spreadsheet, maybe another for a client doc. Then the errors start: duplicate entries, missed comments, mixed-up names, and “wait, which post was this from?” creeping into every row.
It adds up fast. Here’s where it breaks down in real life.
- You spend about 10 minutes per post just collecting comments, and that’s before you organize them.
- Reactions get ignored because they’re tedious to capture, which means you miss who is quietly engaging.
- Once you scale beyond a few posts, your “system” becomes a mix of screenshots, half-filled sheets, and guesswork.
- Manual copy-paste introduces small mistakes that ruin reporting later, especially when you’re trying to de-dupe names.
The Solution: Scrape Comments + Reactions Into Separate Sheet Tabs
This n8n workflow uses Browserflow to extract all comments and reactions from a public LinkedIn post, then saves them into Google Sheets in an organized way. You start with a simple “Posts” tab that holds LinkedIn post URLs. When you run the workflow, it grabs the next post that hasn’t been scraped yet, marks it as processed, and sends the URL to Browserflow for extraction. From there, the workflow splits the results into two streams, so comments and reactions never get mixed together. Finally, it appends each item to the right tab in your sheet, building a clean dataset you can tag, filter, and report on without touching LinkedIn again.
The workflow starts with a manual run in n8n. It pulls the next unscripted post URL from Google Sheets, scrapes the engagement through Browserflow, then writes everything back into two tabs: Comments and Reactions.
What You Get: Automation vs. Results
| What This Workflow Automates | Results You’ll Get |
|---|---|
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Example: What This Looks Like
Say you track 10 LinkedIn posts per week for research. Manually, if you spend roughly 10 minutes collecting comments and another 10 minutes grabbing reactions and cleaning the sheet, that’s about 3 hours weekly. With this workflow, you paste post URLs into the Posts tab, click Execute, and let Browserflow scrape. Your “human time” becomes closer to 2 minutes per post, so you get back around 2.5 hours every week.
What You’ll Need
- n8n instance (try n8n Cloud free)
- Self-hosting option if you prefer (Hostinger works well)
- Google Sheets to store Posts, Comments, and Reactions.
- Browserflow for scraping LinkedIn post engagement.
- Browserflow API key (get it from your Browserflow account dashboard)
Skill level: Beginner. You’ll connect credentials, copy a sheet template, and map a couple of fields.
Don’t want to set this up yourself? Talk to an automation expert (free 15-minute consultation).
How It Works
You run it on demand. A manual trigger starts the workflow, which is perfect when you’re doing a research batch and want the data now.
It pulls the next post URL from your sheet. n8n reads your Google Sheets “Posts” tab, finds a post that hasn’t been scraped yet, and grabs that link so you don’t have to paste it into tools over and over.
The workflow marks that row as processed. This is the small detail that makes it usable long-term. Your “Posts” tab becomes a queue, not a list you babysit.
Browserflow extracts engagement, then n8n separates it cleanly. Comments are split into one stream and appended into the Comments tab. Reactions go into the Reactions tab. You end up with two datasets that are easy to filter and analyze.
You can easily modify the columns you store to include extra metadata (like post topic, campaign name, or internal tags) based on your needs. See the full implementation guide below for customization options.
Step-by-Step Implementation Guide
Step 1: Configure the Manual Trigger
This workflow starts manually so you can test and run LinkedIn extraction on demand.
- Add the Manual Run Trigger node as the workflow trigger.
- Leave default settings, then connect Manual Run Trigger to Retrieve Post Link.
Step 2: Connect Google Sheets
Configure all Google Sheets nodes to read the next post, mark it processed, and store extracted leads.
- Open Retrieve Post Link and select the document Document ID value
[YOUR_ID]with sheet Sheet NamePosts. - Set the filter in Retrieve Post Link to use scraped_at so only unprocessed rows are returned.
- Credential Required: Connect your googleSheetsOAuth2Api credentials to Retrieve Post Link.
- Open Mark Row Processed and set Operation to
update. - Set the column mappings in Mark Row Processed to row_number
{{ $('Retrieve Post Link').item.json.row_number }}and scraped_at{{$now}}. - Credential Required: Connect your googleSheetsOAuth2Api credentials to Mark Row Processed.
- Open Add Comments to Sheet and set Operation to
appendwith Sheet NameComments. - Map fields in Add Comments to Sheet: name
{{ $json.name }}, likes{{ $json.likes }}, comment{{ $json.comment }}, img_url==IMAGE("{{ $json.image_url }}";1), replies{{ $json.replies }}, tagline{{ $json.tagline }}, relation{{ $json.relation }}, linkedin_url{{ $json.linkedin_url }}. - Credential Required: Connect your googleSheetsOAuth2Api credentials to Add Comments to Sheet.
- Open Add Reactions to Sheet and set Operation to
appendwith Sheet NameReactions. - Map fields in Add Reactions to Sheet: name
{{ $json.name }}, img_url==IMAGE("{{ $json.image_url }}";1), tagline{{ $json.tagline }}, relation{{ $json.relation }}, linkedin_url{{ $json.linkedin_url }}, reaction_type{{ $json.reaction_type }}. - Credential Required: Connect your googleSheetsOAuth2Api credentials to Add Reactions to Sheet.
Posts, Comments, and Reactions exist and match the column schema, or the update/append operations will fail.Step 3: Set Up the Browserflow Extraction
Configure Browserflow to scrape commenters and reactors from the LinkedIn post URL.
- Open Extract Post Profiles and set Operation to
scrapeProfilesFromPostComments. - Set postUrl to
{{ $('Retrieve Post Link').item.json.url }}so the URL comes from the sheet. - Enable addComments and addReactions (both set to
true). - Credential Required: Connect your browserflowApi credentials to Extract Post Profiles.
Step 4: Configure Split and Output Routing
Split comments and reactions into individual items and write them to separate sheets.
- In Separate Comment Items, set Field to Split Out to
comments. - In Separate Reaction Items, set Field to Split Out to
reactions. - Connect Separate Comment Items to Add Comments to Sheet and Separate Reaction Items to Add Reactions to Sheet.
Step 5: Test and Activate Your Workflow
Run a manual test to validate scraping and data writes before activating.
- Click Execute Workflow in n8n to trigger Manual Run Trigger.
- Confirm Retrieve Post Link pulls a row and Mark Row Processed updates
scraped_atwith{{$now}}. - Verify Extract Post Profiles returns
commentsandreactionsarrays. - Check the
CommentsandReactionssheets for new rows appended by Add Comments to Sheet and Add Reactions to Sheet. - When testing is successful, toggle the workflow to Active for production use.
Common Gotchas
- Google Sheets credentials can expire or need specific permissions. If things break, check the n8n Credentials area and confirm the connected Google account still has access to the target spreadsheet.
- If you’re using Wait nodes or external rendering, processing times vary. Bump up the wait duration if downstream nodes fail on empty responses.
- Browserflow outputs can change if LinkedIn updates layouts. If scraping suddenly returns fewer items, check your Browserflow node settings and rerun on a known post to confirm the extraction still matches what you expect.
Frequently Asked Questions
About 30 minutes if your Sheet and Browserflow account are ready.
No. You’ll install the Browserflow community node and connect credentials in n8n.
Yes. n8n has a free self-hosted option and a free trial on n8n Cloud. Cloud plans start at $20/month for higher volume. You’ll also need to factor in Browserflow usage, which depends on how many posts you scrape.
Two options: n8n Cloud (managed, easiest setup) or self-hosting on a VPS. For self-hosting, Hostinger VPS is affordable and handles n8n well. Self-hosting gives you unlimited executions but requires basic server management.
Yes, but plan it in the sheet first. Most people add columns like “Topic,” “Persona,” or “Intent” to the Comments tab, then map those fields when appending rows in the Google Sheets nodes. If you want to filter, you can add an If condition after “Separate Comment Items” to only keep comments that match keywords, or to ignore certain reaction types before “Add Reactions to Sheet.” It’s also common to adjust the “Retrieve Post Link” logic so it only pulls posts from a specific campaign block.
Usually it’s an API key issue or a missing community node install. Confirm the “Browserflow for LinkedIn” node is installed, then regenerate your Browserflow API key and update the credential in n8n. If it still fails, check whether the LinkedIn post is public and reachable; private posts often scrape poorly.
A lot, as long as your Browserflow plan and n8n execution limits support the volume.
If you need reliable branching (comments vs. reactions), queue-like processing from a Posts tab, and the option to self-host, n8n is usually the better fit. Zapier and Make are fine for basic “new row → send message” automations, but scraping and multi-stream processing tends to get clunky or expensive there. Another practical point: this template relies on a community node for Browserflow, which is straightforward in n8n Cloud. If you’re already paying for Zapier, you can still do parts of the workflow, but you’ll likely end up with more moving pieces. Talk to an automation expert if you want a quick recommendation for your exact setup.
Once this is running, LinkedIn engagement stops being “something you’ll organize later.” Your sheet stays clean, your research gets easier, and you can actually use the data.
Need Help Setting This Up?
Our automation experts can build and customize this workflow for your specific needs. Free 15-minute consultation—no commitment required.
