For years, sales professionals have treated LinkedIn connection requests like the first step to social selling success.
The best opportunities often show up in comments and reactions. Instead of search results on LinkedIn, look at the comments and reactions on posts across your social network.
Think about it. If someone comments on an industry thought leader’s post, reacts to a company announcement, or actively participates in relevant groups, they’re already engaged and thinking about your market. These people are more likely to become real connections and ultimately, potential customers.
But how do you find them?
Manually sifting through hundreds of comments is slow, messy, and hard to scale.
PhantomBuster plus ChatGPT turns noisy comment threads into a prioritized list of people worth messaging. Together, they help you transform engagement data into reply-ready prospects all set to enter your sales funnel.
This guide shows you how to analyze LinkedIn comments and turn them into a review-ready lead list using PhantomBuster and ChatGPT.
What you’ll get from this AI workflow
- LinkedIn comments and reactions reveal strong buying signals and lead to warmer outreach than cold connection requests.
- Use PhantomBuster’s LinkedIn comment and reaction automations to collect engagement data, then route it to ChatGPT for scoring, no manual copy-paste.
- PhantomBuster’s LinkedIn Post Comments Export and Reactions Export automations collect commenters and likers from high-engagement posts and send results straight to Google Sheets.
- Have PhantomBuster push the Sheet to ChatGPT (via API or integration) to score reply-readiness (High/Medium/Low), flag decision-makers, and surface pain points.
- Use PhantomBuster’s AI-powered message personalization to reference each prospect’s comment in your invite.
- Keep activity human-paced, reference the original comment in your first message, and track acceptance and reply rates weekly.
Why is LinkedIn engagement a strong intent signal?
Traditional social selling techniques often focus on sending cold connection requests on LinkedIn or exporting lists from LinkedIn Sales Navigator. While that approach generates volume, it doesn’t always guarantee meaningful relationships.
Here’s what makes comments and reactions stronger intent signals:
- Real-time insights: Comments on social media platforms reflect what people are actively thinking about, whether it’s a pain point, trend, or product.
- Warmer intent: Engaged LinkedIn members are closer to your ideal customer profile (ICP) than dormant users.
- Social proof: When someone publicly interacts with a specific post, they’re showing genuine interest in the topic.
- Relationship building: A thoughtful reply to a comment is an organic way to start a conversation without looking like a cold pitch.
In short: Engagers are not just “leads.” They’re reply-ready prospects already primed for conversation.
How do you analyze LinkedIn comments step by step?
Monitoring LinkedIn comments and reactions should be an integral part of your social selling efforts. It supports social listening, sentiment checks, and reputation monitoring so you can prioritize responses.
Here’s a practical automation workflow to analyze LinkedIn comments:
1. Extract comments and reactions with PhantomBuster
To begin with, you need clean data to analyze LinkedIn comments. Start by using PhantomBuster to automatically collect likers and commenters from specific posts or post search results on LinkedIn.
Here’s how sales teams run this workflow:
Step 1: Identify a high-engagement post from your company page, a competitor, or an industry voice.
Step 2: Run PhantomBuster’s LinkedIn Post Comments Export or Reactions Export automations on specific posts or post search results. Use PhantomBuster scheduling to refresh data daily or weekly so you always get the most recent engagement data.
Step 3: Send results directly to Google Sheets with PhantomBuster’s built-in export so you don’t manually download files.
Now you have a dataset of potential buyers already interacting with content related to your market. That makes them more likely to respond to your outreach.
2. Analyze LinkedIn comments with ChatGPT
Have ChatGPT score reply-readiness, flag decision-makers, and group themes (hiring, tools, growth).
Route your Google Sheet to ChatGPT via API or a no-code automation platform and run this prompt to categorize and prioritize prospects from your target audience.
Sample analysis prompt:
Analyze these LinkedIn comments. For each person, rate reply-readiness (High/Medium/Low) based on job relevance, engagement depth, and whether they express pain points or curiosity. Highlight decision makers and rank by priority.
ChatGPT can return a table with reply-readiness, decision-maker status, and priority. Review and spot-check before outreach, then reach out to the ones in the “high” category to build relationships and generate leads.
If you’re analyzing reactions, modify the prompt accordingly:
Analyze these reactions to a LinkedIn post. For each user, rate reply-readiness (High/Medium/Low) based on job relevance and reaction type. Highlight decision makers and rank by priority.
What ChatGPT can do:
- Identify decision-makers by job titles and companies.
- Classify intent signals (for instance, a “like” indicates low intent while a thoughtful comment shows high intent).
- Highlight pain points expressed directly in comments.
- Have ChatGPT group themes, and write them back to your Sheet so reps can filter by topic.
The result?
You’ll move from a broad list to a smaller, high-intent list. Teams report higher reply rates when referencing a prospect’s own comment.
3. Prioritize and personalize outreach
With ChatGPT’s analysis, you can target your outreach better and connect with prospects on a more personal level.
Use the insights to draft a short connection message that references their comment and one relevant benefit. Draft it yourself or use PhantomBuster’s AI-powered message personalization to generate a first draft you can edit before sending.
Example workflow 1:
- Comment: “Scaling SDR teams is always messy. Hiring is our biggest bottleneck.”
- PhantomBuster + ChatGPT analysis: High-intent → Sales leader at SaaS company.
- Outreach message:
Hi [Name], saw your comment about SDR scaling challenges. Totally agree. We’ve helped similar SaaS teams shorten hiring cycles. Would love to connect.
Example workflow 2:
- Comment: “We’ve been testing multiple CRM tools, but none of them integrate smoothly with our stack.”
- PhantomBuster + ChatGPT analysis: High-intent → VP of Operations at a mid-size tech firm.
- Outreach message:
Hi [Name], noticed your comment on CRM integrations. That’s a common headache we hear from ops leaders. Teams cut manual handoffs by consolidating steps into one workflow. Happy to share what worked if you’re exploring options.
Example workflow 3:
- Comment: “Employee onboarding is eating up so much time. We’re looking for ways to automate parts of the process.”
- PhantomBuster + ChatGPT analysis: Medium-to-High intent → HR Director at a growing startup.
- Outreach message:
Hi [Name], saw your point about onboarding challenges on a recent LinkedIn post. Many of our clients faced the same issue before automating. We’ve built solutions that cut onboarding time in half. Would love to connect and compare notes.
Pro Tip: Referencing their specific comments demonstrates genuine interest and builds trust immediately. This is social selling success: real conversations based on authentic engagement.
Real-world workflow example
Here’s how a SaaS team ran this workflow.
Here’s how a SaaS sales team analyzed thousands of comments, identified qualified leads, and improved connection acceptance rates:
Scenario:
A SaaS sales team monitors a competitor’s CEO post that generates 1,200 comments.
Workflow used:
- Step 1: Extract all comments with PhantomBuster.
- Step 2: Run ChatGPT analysis to rank reply-readiness.
- Step 3: Example: 150 high-intent prospects (decision-makers mentioning hiring, tools, or growth).
- Step 4: Sales reps send personalized connection requests and effective follow-ups.
Results:
- Higher connection acceptance rates when referencing the original comment.
- Dozens of conversations started by referencing the original comment.
- Three demos booked in the first week.
We use PhantomBuster to strategically build and cultivate relationships with targeted personas for our B2B influencers. The platform enables us to precisely identify prospects by their LinkedIn job titles and roles, and then create personalized connection requests. Our results speak volumes: higher acceptance rates than other approaches.
Patrick Spencer, VP of Corporate Marketing & Research at Kiteworks
Core tools for this workflow
| Tool | Purpose | Why it matters |
|---|---|---|
| PhantomBuster | Collect LinkedIn comments and reactions, export to Google Sheets, trigger ChatGPT scoring | Automates data collection and integrates the workflow end-to-end |
| ChatGPT | Analyze comments and classify intent | Transforms raw comment data into actionable insights |
| Google Sheets | Organize data for review | Use PhantomBuster’s Google Sheets export. Optionally, connect that Sheet to ChatGPT via API or a no-code automation |
| LinkedIn Sales Navigator (optional) | Enrich profiles and segment ICPs | Offers several filters for sales professionals to segment the target market |
| CRM (HubSpot or Salesforce, optional) | Track outreach results | Measures deal progress and conversion rates |
Best practices for social selling success
The PhantomBuster + ChatGPT workflow can change the way you approach lead generation and outreach.
But you need to follow a few ground rules to avoid LinkedIn restrictions, build meaningful connections, and win long-term customers.
These include:
- Don‘t spam everyone: Focus on quality, not just volume. Start small and scale slowly.
- Respect limits: Respect LinkedIn’s evolving safety limits. Keep invites well below your account’s typical activity, vary timing, and monitor warnings.
- Engage naturally: Reference the original comment or reaction in your outreach.
- Provide insights: Share valuable content or timely updates instead of jumping directly to a pitch.
- Measure performance: Track acceptance rates, response rates, and downstream conversions to optimize your social selling strategy.
FAQs
How many comments should I analyze at once?
Start by analyzing 100 to 200 comments per post. As your process matures, scale to 1,000+ comments with automation. Analyzing more comments ensures broader audience diversity and minimizes sampling bias.
Can ChatGPT really analyze tone and intent?
Yes. With the right prompts, generative AI tools like ChatGPT can flag curiosity, pain points, and engagement depth.
It helps you identify the most reply-ready prospects who are already showing buying intent or a willingness to engage. That, in turn, reduces the need for cold outreach with social selling tools.
Are third-party automation tools against LinkedIn’s terms of service?
Always review LinkedIn’s Terms of Service and use PhantomBuster responsibly. Our safe-usage controls (scheduling, rate limits, random delays) help you act conservatively, but no tool can guarantee against restrictions. Keep activity human-paced and prioritize personalization.
Respect LinkedIn’s limits and focus on genuine conversations—reference the prospect’s comment in your first message.
Can social sellers use this workflow for brand reputation monitoring?
Yes. Set PhantomBuster’s LinkedIn Post Comments Export to run weekly on posts that mention your brand or competitors, then have ChatGPT flag praise, complaints, and emerging themes.
Instead of ranking “reply-readiness,” ChatGPT can flag praise, complaints, or emerging themes. You can use this analysis to identify risks, amplify positive mentions, and engage with your audience to protect and strengthen brand reputation.
Does analyzing LinkedIn comments work outside sales?
Yes. Analyzing LinkedIn comments isn’t just helpful for sales teams. Recruiters, marketers, and community builders can use the automation workflow in this guide to spot job seekers, identify potential buyers, or build stronger relationships with followers.
Small businesses can use this to find local buyers engaging with industry posts and start context-rich conversations.
Engagement-first outreach consistently drives warmer conversations
Analyzing LinkedIn comments and reactions is one of the most overlooked ways to find reply-ready prospects. With PhantomBuster to collect the data and ChatGPT to analyze it, sales teams can turn noisy threads into actionable lead lists.
The approach aligns with modern social selling goals: building real connections, offering useful content, and creating meaningful relationships that lead to more sales and revenue.
Want to try it? Start a 14-day free trial and build your first engagement-sourced lead list in minutes.