domains / marketing / social-comment-mining / comment-mining

comment-mining

Skill card

combined

Both copies describe the same task—mining public social comments for voice-of-customer signals—but each contains material steps the other lacks. The gooseworks copy adds a distinct buying-intent strength scale, a broader coding taxonomy (e.g., churn risk, workaround, exact product language), and a requirement to report prevalence as sample counts by platform/source type. The ScrapeCreators copy adds concrete API endpoints per platform, a required environment variable, a specific output template, and platform-specific pitfalls (e.g., read Reddit post context). Crowning one would hide these real alternatives.

Repositories

Other copies

Combined skill

---
name: comment-mining
description: Mine comments and replies on social posts, videos, and ads for recurring customer language, questions, objections, desired outcomes, complaints, product requests, purchase signals, and creative opportunities. Use for voice-of-customer research grounded in linked source evidence.
---

# Comment Mining

Turn public comment threads into evidence a growth team can use.

## Inputs

- Brand/product and research question.
- Post, reel, video, or ad URLs; or creators/competitors to sample.
- Target market, platforms, time window, and desired sample size.

## Comment Sources

| Platform | Endpoint |
|---|---|
| TikTok comments | `/v1/tiktok/video/comments` |
| TikTok replies | `/v1/tiktok/video/comment/replies` |
| YouTube comments | `/v1/youtube/video/comments` |
| YouTube replies | `/v1/youtube/video/comment/replies` |
| Instagram comments | `/v2/instagram/post/comments` |
| Facebook comments | `/v1/facebook/post/comments` |
| Facebook replies | `/v1/facebook/post/comment/replies` |
| Reddit comments | `/v1/reddit/post/comments` |
| Rumble comments | `/v1/rumble/video/comments` |

## Workflow

1. **Fetch comments**
   - Use `scrapecreators-api` to collect post context, top-level comments, and replies from the relevant platforms. Use the post/video URL whenever possible.
   - Paginate when the endpoint supports it and the user wants depth.
   - Preserve comment text, author if public, like/upvote count, timestamp, and source URL.
   - Sample across multiple posts and creators instead of overfitting to one viral thread.
   - For Reddit, read both the original post and comments.

2. **Clean lightly**
   - Remove obvious spam, duplicate comments, tag-only replies, and giveaways unless they are the subject of the study.
   - Preserve parent-and-reply context where it changes meaning.
   - Keep slang, misspellings, and emotional wording if it is useful customer language.
   - Do not over-normalize exact quotes.

3. **Classify each useful comment**
   Code each useful comment into one or more buckets:
   - pain
   - desired outcome
   - objection
   - question
   - comparison
   - use case
   - purchase intent
   - product request
   - workaround
   - delight
   - complaint
   - churn risk
   - exact product language
   - confusion
   - controversy/debate
   - jokes/memes/culture signals

4. **Mark buying-intent strength separately**
   Use this scale: curiosity, consideration, price or availability question, comparison, stated purchase, repeat use, and recommendation.

5. **Cluster themes**
   - Group semantically similar statements while retaining representative wording, the post context, and source links.
   - Score themes by frequency and intensity.
   - Highlight exact quotes for each theme.

6. **Report prevalence**
   - Report prevalence as sample counts by platform and source type, not market-wide percentages.
   - Label sample size and confidence.
   - Separate one loud comment from a repeated pattern.

7. **Turn insights into actions**
   Convert the strongest clusters into testable messages, hooks, FAQ topics, product questions, or research follow-ups. Depending on the user's goal, produce:
   - content ideas
   - FAQ ideas
   - landing page copy angles
   - product ideas
   - objection-handling bullets
   - sales/support notes

## Output Format

```markdown
# Comment Mining Report

## Summary
- Source(s): {urls}
- Comments analyzed: {count}
- Confidence: High/Medium/Low

## Top Themes
| Theme | Type | Frequency | Intensity | Representative quote | Source links |
|---|---|---:|---|---|---|

## Audience Questions
- "..."

## Objections and Concerns
- **Objection:** ...
  - Evidence: "..."
  - Response angle: ...

## Buying Intent / Demand Signals
- "..."

## Exact Language to Reuse
- "..."
- "..."

## Content Ideas From Comments
1. ...
2. ...

## Limitations and Gaps
- ...
```

## Quality Guardrails

- Label sample size and confidence.
- Separate one loud comment from a repeated pattern.
- Preserve exact quotes for useful language.
- Avoid claiming broad market sentiment from one post's comments.
- Call out moderation/platform bias when relevant.
- Never expose private information, infer sensitive traits, or claim the sample represents all customers.

## Common Pitfalls

- Do not flatten comments into generic sentiment. The value is in questions, objections, and exact wording.
- Do not include personally identifying details unless they are already public and necessary.
- Do not treat bot/spam comments as audience signal.
- Do not skip Reddit post context. For Reddit, read both the original post and comments.