Find content ideas in video comments
Analyze viewer reactions and collect suggestions for future episodes in a separate editorial list.
How this works
Analyze viewer reactions and collect suggestions for future episodes in a separate editorial list. Comments contain questions, jokes, complaints, and requests mixed together. The goal is a reviewable editorial backlog grounded in what viewers actually asked for, not an automatic vote count that rewards spam or repeated comments.
Collect permitted comments for selected videos, keep thread context, and separate topic requests from sentiment. Use the assistant to propose categories and consolidate equivalent requests. Editors decide which ideas fit the channel and should become assignments; the original comments remain available privately for verification.
Before you start
Collection scope
Choose videos, dates, languages, and whether replies are included.
Access method
Use an authorized API or a supplied export; if unavailable, analyze a documented manual sample.
Editorial criteria
Define audience fit, novelty, expertise, and feasibility for future episodes.
Prepare the inputs
- comment_records
- Required
CSV or JSON
Include comment ID, video ID, text, timestamp, and parent ID.
- video_context
- Required
Title and topic per video
Interpret comments relative to what the episode actually covered.
- idea_taxonomy
- Required
Topic labels
Allow multi-topic comments and an uncertain category.
- editorial_backlog
- Required
Existing idea list
Use it to detect already planned or previously covered requests.
- collection_metadata
- Required
Window and completeness
Record pagination gaps and deleted or unavailable comments.
Implementation, step by step
Validate the comment set
- Deduplicate by stable comment ID and retain reply relationships.
- Remove known spam from analysis while recording the exclusion rule; do not discard criticism merely because it is negative.
Checkpoint: A clean set with a documented sampling boundary.
Read comments in context
- Attach the video topic and relevant parent comment before classification.
- Distinguish literal requests, sarcasm, support questions, and general reactions without guessing hidden motives.
Checkpoint: Context-aware comment labels.
Extract candidate ideas
- Convert explicit requests and well-supported recurring questions into concise editorial propositions.
- Keep the supporting comment references and preserve minority questions that may be useful despite low volume.
Checkpoint: An evidence-linked candidate list.
Consolidate equivalent requests
- Group requests that ask the same underlying question, while keeping meaningfully different audiences or constraints separate.
- Count distinct contributing records and, where permitted, avoid treating one prolific commenter as many independent people.
Checkpoint: A deduplicated idea backlog.
Review editorial fit
- Compare candidates with existing episodes and the team's content remit.
- Explain why an idea is useful, what new evidence it needs, and whether it belongs in support rather than editorial work.
Checkpoint: A prioritized review board.
Track decisions and outcomes
- Record chosen, deferred, answered elsewhere, or rejected status with a short reason.
- When an episode is commissioned, link it to the originating idea and later review whether it answered the request.
Checkpoint: A maintained editorial feedback loop.
Decisions along the way
Comment asks for account-specific help
Route it to the approved support process rather than turn private details into content.
Several requests sound similar but target different experience levels
Keep separate briefs or explicitly design a beginner and advanced version.
Sentiment is ambiguous
Mark uncertain; topic usefulness need not depend on a positive or negative label.
What you will produce
audience-idea-board.csv
CSVEditors can filter by topic and review the actual request evidence.
Include: idea_id · question · topic · comment_refs · request_count · editorial_status
collection-notes.md
MarkdownExplains which audience signals the board represents.
Include: scope · exclusions · gaps · classification_rules
Check the result
- Trace each idea to actual comment language.
- Inspect merged groups for distinct questions accidentally collapsed.
- Check counts exclude duplicate records and known spam.
- Confirm support issues and personal information are not exposed in public briefs.
When something goes wrong
API pagination is incomplete
Mark the sample partial and resume from the last verified cursor.
Translation changes a request's meaning
Retain the original text and ask a fluent reviewer about the uncertain phrase.
The assistant invents popular demand
Remove unsupported frequency claims and show the actual contributing count.
A worked example
SYNTHETIC EXAMPLE · NOT A REPORTED BUSINESS RESULT
Request
Find future episode ideas in last month's comments.
Inputs
Synthetic example. Synthetic C1 asks for a beginner pricing example; C2 repeats that request; C3 asks why an individual payment failed.
Expected result
Synthetic expected result. Create one beginner-pricing idea supported by C1 and C2. Route C3 to support and exclude its account-specific details from the editorial board.
Run a small pilot
Start small
A few videos with a mix of explicit requests, replies, and ambiguous comments.
Record the baseline
Record manual sorting time and ideas editors consider genuinely actionable.
Agree success criteria
Agree acceptable grouping accuracy and evidence coverage before expanding collection.
Stop or revise
Stop prioritization when the sample boundary or source references cannot be established.
Keep the scope clear
- Public comments are evidence for editorial questions, not permission to profile commenters.
- Drafting a backlog does not authorize replying to viewers or publishing a promised episode.
Check before using
Check irony, duplicates, and context; commenters represent the active part of the audience.
Business activities
- Create and publish content
- Research customers and markets
FROM A RECIPE TO YOUR WORKFLOW
Bring the company context together.
This workflow needs reliable inputs before it needs a better prompt. Moat8 Vault brings working information into a readable archive for the AI tools you use.
For this recipe: Channel or video comments · Sentiment categories
Start with one recurring task, agree which sources to use, and review the first result with your team.
Discuss this workflow