LinkedIn Comments

LinkedIn Comment signals capture when a prospect engages meaningfully on someone else's post — revealing their interests, opinions, and intent through comments rather than original content.

Comments often expose more honest reactions than polished posts. When a VP of Engineering comments on a post about migrating off legacy infrastructure, that's a stronger buying signal than if they'd published a generic thought leadership piece. We analyze both the comment itself and the parent post to understand the full context of the engagement.

We run comment analysis on the posts generated from our dynamic monitored audience (the same 4M+ profile pool that powers LinkedIn Posts). We optimize for comment depth under a post based on virality — higher-engagement posts attract more comments from decision-makers, which means richer signal.

Use cases:

Engagement measurement — track which topics and conversations your prospects are actively engaging with
Relational intelligence — detect when a prospect is commenting on a competitor's content, an industry analyst's post, or a peer's discussion about vendor evaluation

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See real delivered data → Sample Files

Filter by topic — This signal uses Tags Taxonomy (300+ values) rather than subtypes for topic-based filtering. Use tags alongside pain_points, initiatives, and technologies_mentioned for targeting and routing.

Example Signal

What a single entry looks like in a delivered signal file:

{
  "signal_id": "e9c17f84-3a2b-4d68-9e5c-81b4f6a2d703",
  "batch_id": "2026-03-15-00-00-00",
  "signal_type": "autobound-linkedin-comments",
  "signal_subtype": "linkedinComment",
  "detected_at": "2026-03-14T14:08:33.227Z",
  "association": "contact",
  "company": {
    "name": "Databricks",
    "domain": "databricks.com",                    // match on domain
    "linkedin_url": "linkedin.com/company/databricks",
    "industries": ["Software Development"],
    "employee_count_low": 5001,
    "employee_count_high": 10000
  },
  "contact": {
    "name": "Marcus Chen",
    "first_name": "Marcus",
    "last_name": "Chen",
    "email": "[email protected]",         // match on email
    "job_title": "VP of Platform Engineering",
    "linkedin_url": "linkedin.com/in/marcuschen-eng"  // or match on LinkedIn URL
  },
  "data": {
    "comment_text": "We ran into this exact problem scaling our internal observability stack. Ended up evaluating three vendors before realizing we needed to...",
    "comment_summary": "Marcus Chen shares experience scaling observability and evaluating vendors, expressing frustration with existing tooling limitations...",
    "comment_intent": "vendor_evaluation",
    "comment_url": "https://www.linkedin.com/feed/update/urn:li:comment:(activity:7170234567890123456,7170567890123456789)",
    "comment_num_likes": 34,
    "comment_num_comments": 8,
    "signal_quality": 0.87,
    "pain_points": [
      { "topic": "observability stack scaling limits", "intensity": 0.84 },
      { "topic": "vendor lock-in with current tooling", "intensity": 0.71 }
    ],
    "initiatives": [
      { "topic": "evaluating observability vendors", "urgency": 0.89 }
    ],
    "technologies_mentioned": [
      { "name": "Datadog", "status": "replacing" },
      { "name": "OpenTelemetry", "status": "evaluating" }
    ],
    "competitors_mentioned": [
      { "name": "Datadog" },
      { "name": "New Relic" }
    ],
    "relationship_context": {
      "inferred_relationship": "industry_peer",
      "confidence": 0.73
    },
    "parent_post": {
      "signal_id": "f2d83b91-4c7a-4e55-a8f2-19c6d7b3e504",
      "post_text": "The observability market is at an inflection point. Companies are spending 40% of their cloud budget on monitoring tools that still can't...",
      "post_summary": "Industry analyst discusses observability cost crisis and emerging alternatives to incumbent vendors...",
      "post_url": "https://www.linkedin.com/feed/update/urn:li:activity:7170234567890123456",
      "posted_date": "2026-03-12",
      "poster_name": "Sarah Liu",
      "poster_job_title": "Principal Analyst",
      "poster_company_name": "Forrester",
      "poster_company_description": "Research and advisory firm...",
      "num_likes": 1247,
      "num_comments": 89,
      "tags": ["observability", "cloud cost optimization", "vendor evaluation"],
      "pain_points": [
        { "topic": "observability cost overruns", "intensity": 0.91 }
      ],
      "initiatives": [
        { "topic": "next-gen observability platforms", "urgency": 0.78 }
      ],
      "technologies_mentioned": [
        { "name": "OpenTelemetry", "status": "mentioned" }
      ],
      "competitors_mentioned": [
        { "name": "Datadog" },
        { "name": "Splunk" }
      ]
    }
  }
}

Field Reference

Standard envelope and entity fields are shared across all signals — see Schema and Resolution. The fields below are specific to this signal:

Signal-Specific Fields

The data object contains everything unique to this signal type — the intelligence extracted from the LinkedIn comment and its parent post.

Field	Type	Description
`comment_text`	string	The full text of the contact's comment. Useful for personalized outreach referencing their exact words and stated experience
`comment_summary`	string	One-line summary of the comment's relevance. Designed to be shown as a notification. Typically 10–20 words describing what the contact said and why it matters
`comment_intent`	string	Classified intent behind the comment: `vendor_evaluation`, `pain_sharing`, `knowledge_seeking`, `opinion`, `endorsement`, `experience_sharing`. Useful for routing signals to the right sales motion
`comment_url`	string (URL)	Direct link to the comment on LinkedIn. Useful for verifying context and reading the full thread
`comment_num_likes`	integer	Likes on the comment itself. High engagement indicates the comment resonated with the community
`comment_num_comments`	integer	Replies to the comment. Active reply threads indicate the prospect is deeply engaged in the conversation
`signal_quality`	float (0.0–1.0)	Overall quality score combining comment depth, intent strength, and relevance. Useful for prioritization
`pain_points`	array[object]	Pain points expressed in the comment. Each entry has `topic` and `intensity` (0.0–1.0). Extracted from the commenter's words specifically
`pain_points[].topic`	string	Description of the pain point expressed
`pain_points[].intensity`	float (0.0–1.0)	How strongly expressed. Higher = more urgent
`initiatives`	array[object]	Initiatives the contact mentions in their comment. Each entry has `topic` and `urgency` (0.0–1.0)
`initiatives[].topic`	string	Description of the initiative
`initiatives[].urgency`	float (0.0–1.0)	How time-sensitive the initiative appears
`technologies_mentioned`	array[object]	Technologies referenced in the comment. Each entry has `name` and `status` (`in_use`, `evaluating`, `replacing`, `mentioned`)
`technologies_mentioned[].name`	string	Name of the technology
`technologies_mentioned[].status`	string	The contact's relationship to the technology
`competitors_mentioned`	array[object]	Competitors the contact names in their comment. Each entry has `name`
`competitors_mentioned[].name`	string	Competitor name
`relationship_context.inferred_relationship`	string	Inferred relationship between the commenter and the original poster: `colleague`, `industry_peer`, `customer`, `vendor`, `unknown`. Useful for understanding why this person engaged with this post
`relationship_context.confidence`	float (0.0–1.0)	Confidence in the relationship inference
`parent_post.post_text`	string	Text of the original post that was commented on. Provides context for interpreting the comment
`parent_post.post_summary`	string	Summary of the parent post's content and relevance
`parent_post.post_url`	string (URL)	Link to the original post
`parent_post.posted_date`	string (date)	Date the parent post was published
`parent_post.poster_name`	string	Name of the person who wrote the original post
`parent_post.poster_job_title`	string	Job title of the original poster. Useful for understanding what kind of content the prospect engages with
`parent_post.poster_company_name`	string	Company of the original poster
`parent_post.poster_company_description`	string	Brief description of the poster's company
`parent_post.signal_id`	string	Signal ID of the parent post, if it was also captured as a separate signal. Useful for cross-referencing
`parent_post.num_likes`	integer	Engagement on the parent post
`parent_post.num_comments`	integer	Comment count on the parent post
`parent_post.tags`	array[string]	Topic tags from the parent post
`parent_post.pain_points`	array[object]	Pain points discussed in the parent post (same structure as top-level pain_points)
`parent_post.initiatives`	array[object]	Initiatives discussed in the parent post (same structure as top-level initiatives)
`parent_post.technologies_mentioned`	array[object]	Technologies in the parent post (same structure as top-level technologies_mentioned)
`parent_post.competitors_mentioned`	array[object]	Competitors named in the parent post

Timing & Delivery

detected_at is when we processed the comment. Use parent_post.posted_date for the original post date.
One signal per contact per comment. If a prospect comments multiple times on the same post, each comment generates a separate signal.
Each delivery arrives in a timestamped folder. Treat all signals in a new folder as recent — no need to diff against prior deliveries.

Coverage

Refresh: Monthly
Coverage: 4M+ LinkedIn profiles (comments by senior decision-makers)
Best for: Identifying prospects actively evaluating vendors, spotting pain points expressed in their own words, understanding which thought leaders and topics a prospect engages with

Contact Sales →

See real delivered data → Sample Files

Example Signal

Field Reference

Signal-Specific Fields

Timing & Delivery

Coverage