How to Find YouTube Video Ideas From Comments: We Analyzed 27,555

Most advice about finding YouTube video ideas in comments starts with one instruction:

Look for people asking you what video to make next.

That is useful.

It is also incomplete.

We analyzed 27,555 public YouTube comments across 1,954 videos and 44 channels to find out what audience demand actually looks like when viewers write back to creators.

The biggest finding was simple:

The best content ideas in YouTube comments usually do not arrive as direct content requests.

Across the dataset:

• 9,677 comments, or 35.1%, contained at least one actionable audience-demand signal
• 3,333 were how-to questions
• 2,665 described pain points
• 1,637 raised objections or constraints
• only 1,302 were direct content requests
• 507 identified a missing capability
• 437 asked for a tool
• 217 asked for a comparison

How-to questions and pain points together appeared in:

60.6% of all signal-bearing comments.

Direct content requests appeared in only:

13.5%.

So if your comment research consists of searching for:

"Can you make a video about..."

you are ignoring most of the usable demand.

There was another surprise.

The useful comments were not necessarily the popular comments.

Signal-bearing comments had:

• median likes: 0
• 75th percentile: 1 like
• comments with at least one like: 42.3%

Comments with no classified audience-demand signal actually received slightly more engagement:

• median likes: 0
• 75th percentile: 2 likes
• comments with at least one like: 48.0%

Even among the most-liked comments in the dataset, actionable signals were slightly less common than in the corpus overall.

That matters because one of the most common comment-mining workflows is:

Sort by top comments and study the most-liked ones.

Our data suggests that can miss exactly the quiet questions, frustrations, and requests that make comments valuable as a content-research source.

The practical lesson is:

Do not mine comments for popularity. Mine them for unresolved viewer needs.

Key Findings

The result changes the way comment research should be done.

The strongest idea is often not:

"Please make a video about X."

It may be:

"How do I do X?"

or:

"I keep failing when I reach Y."

or:

"This works, but what if I do not have Z?"

or:

"Which tool should I use for this?"

Each one reveals a different type of unmet demand.

The Direct Answer

How do you find YouTube video ideas from comments?

Look for repeated unresolved viewer needs, not just explicit video requests.

The strongest comment signals are:

1. How-to questions
2. Pain points
3. Objections
4. Content requests
5. Missing capabilities
6. Tool requests
7. Comparison requests

Then group similar comments across multiple videos and ask:

Does this reveal a problem large enough for a standalone video?

The most important step is what comes next.

A comment is evidence that one viewer cared enough to write something.

It is not proof that thousands of viewers want a video.

Use comments to discover hypotheses.

Then validate those hypotheses against:

• competitor performance
• existing successful videos
• search or topic demand
• your audience fit
• your channel's previous results

Comments tell you what to investigate.

They should not be the only reason you produce.

What We Actually Analyzed

This research comes from public YouTube comments collected through OverseerOS research workflows.

The current versioned classification cohort contained exactly:

• 27,555 classified comments
• 1,954 source videos
• 44 channels

The comment classification was heavily English-language:

26,336 comments, or 95.6%, were classified as English.

The comments themselves were published between May and August 2026, while the videos they belonged to extended further back.

This is important because the study represents:

a creator-focused YouTube research corpus

rather than a random sample of every type of comment across the entire platform.

The topic distribution strongly reflects creator interests such as:

• editing
• monetization
• channel problems
• faceless content
• YouTube growth
• automation
• niches
• titles
• scripts

So the exact topic percentages should not be generalized to gaming, music, sports, celebrity, or every other YouTube audience.

The more transferable finding is the shape of audience demand:

questions, pain, objections, requests, missing capabilities, tool decisions, and comparisons.

How the Comments Were Classified

Each comment was evaluated against a fixed audience-intelligence taxonomy.

A comment could contain no actionable signal, one signal, or more than one.

The seven signal categories used in this analysis were:

Among the 9,677 signal-bearing comments:

95.6% received one signal category.

Another:

4.4%

contained two.

That tells us most useful comments were interpretable as one dominant viewer need.

You do not necessarily need a complicated twenty-label taxonomy to make comment research useful.

Finding 1: Roughly One in Three Comments Contained an Actionable Signal

Out of:

27,555 comments

we identified:

9,677

with at least one audience-demand signal.

That is:

35.1%.

This does not mean every third comment deserves a video.

A single question might be:

• too specific
• already answered
• irrelevant to your positioning
• technically impossible
• based on a misunderstanding
• interesting to only one person

But it does tell us something important about comment sections.

They are not merely:

• praise
• jokes
• reactions
• arguments
• emojis
• compliments

A substantial portion contained usable strategic information.

That makes comments one of the few public research sources where viewers can tell creators, in their own language:

what is still unresolved.

Finding 2: How-To Questions Were the Largest Signal

The largest category was:

how-to questions.

We found:

3,333.

That is:

12.1% of all classified comments

and:

34.4% of signal-bearing comments.

This matters because the viewer is doing more than saying:

"Good video."

They are revealing a job they still need completed.

A hypothetical example:

How do I apply this if my channel is brand new?

That comment contains at least three pieces of useful information:

• the viewer understood the broader idea
• the current video did not fully solve their specific case
• "brand new channel" may represent a separate content angle

Now imagine the same underlying question appears repeatedly across five videos.

That becomes much more interesting.

The opportunity might be:

How to Apply [Strategy] When Your YouTube Channel Has No Audience Yet

The comment did not hand you that final title.

It handed you the unresolved job.

That is more valuable.

Finding 3: Pain Points Were Nearly as Important as Questions

The second-largest signal was:

pain points.

We found:

2,665 comments.

That is:

9.7% of the entire corpus

and:

27.5% of signal-bearing comments.

A pain point sounds different from a question.

The viewer may never write:

Can you make a tutorial about this?

Instead, they say something conceptually like:

I can find ideas, but none of them get views.

That is already a content brief.

The surface problem:

finding ideas

may not be the real issue.

The deeper problem could be:

validating ideas before production.

That could become:

Why Your YouTube Ideas Look Good but Still Get No Views

or:

How to Validate a YouTube Video Idea Before You Make It

Pain comments are useful because they reveal the gap between:

what the viewer knows

and:

what the viewer can successfully do.

Finding 4: 60.6% of Useful Comments Were Questions or Pain

When we combine comments containing either:

• a how-to question
• a pain point

we get:

5,863 comments.

That represents:

60.6% of all signal-bearing comments.

This may be the most useful result in the study.

Creators often think audience research means asking:

What videos do you want me to make?

But viewers are usually better at explaining:

their problem

than designing:

your content strategy.

That means the researcher's job is translation.

The audience says:

I cannot get this to work.

You ask:

What knowledge, decision, demonstration, comparison, or workflow is missing?

That missing layer becomes the video.

Finding 5: Direct Content Requests Were Only 13.5% of the Signal

We classified:

1,302 comments

as content requests.

That is:

4.7% of all comments

and:

13.5% of signal-bearing comments.

Direct requests are valuable.

They are also the obvious signal.

The bigger lesson is what happens if you only search for them.

You miss more than:

86%

of the signal-bearing comments.

Imagine you search comments only for phrases such as:

• make a video about
• do a video on
• can you cover
• please explain
• part two
• next video

You will find useful ideas.

But you will systematically ignore viewers who express demand through:

• confusion
• frustration
• objections
• decisions
• workflow problems
• failed attempts

That is why the best comment mining system should classify intent, not merely search for request phrases.

Finding 6: Objections Are an Underrated Content Source

We found:

1,637 objections.

That represents:

16.9% of signal-bearing comments.

An objection is strategically different from a question.

Imagine a creator says:

You should upload more often.

A viewer replies:

That is impossible for documentary channels because one video takes two weeks to edit.

That is not necessarily a request.

It reveals a tension:

generic advice versus production reality.

That could become:

Does Posting More Often Actually Help on YouTube?

or:

Quality vs Quantity on YouTube: What Should Slow Channels Do?

Objections are valuable because they often expose where generic advice breaks.

Those are excellent places to create:

• myth-busting videos
• constraint-specific tutorials
• evidence studies
• comparisons
• alternative workflows

A strong objection tells you:

The current answer does not survive this viewer's reality.

That is information gain.

Finding 7: Tool and Comparison Requests Are Smaller but High-Intent

We found:

437 tool requests

and:

217 comparison requests.

They were much less common than how-to questions or pain points.

But they can be commercially useful.

A tool request means the viewer may already understand the goal.

Their next problem is:

What should I use?

A comparison request moves even closer to a decision:

Should I use A or B?

Those can become high-intent videos such as:

• best tools for a specific job
• A vs B
• free vs paid
• beginner vs professional workflow
• which tool fits which creator

The raw frequency is lower.

The decision intent can be higher.

Do not rank content ideas only by how many comments fall into each category.

Rank them partly by:

what decision the viewer is trying to make.

Finding 8: The Most Useful Comments Were Not the Most-Liked Comments

This finding directly challenges a common research shortcut.

Signal-bearing comments had:

0 median likes.

Their 75th percentile was:

1 like.

Their 90th percentile was:

5 likes.

Only:

42.3%

had at least one like.

Non-signal comments had:

• median likes: 0
• 75th percentile: 2
• 90th percentile: 6
• at least one like: 48.0%

So the comments we classified as strategically useful were actually less likely to have likes.

We ran another check.

The top decile of comments by likes began around:

6 likes.

Among those highly liked comments:

32.0%

contained an actionable signal.

Across the full corpus:

35.1%

did.

This is not a massive difference.

But it clearly fails to support the assumption:

More likes means more useful content demand.

Likes can reward:

• humor
• agreement
• emotional reactions
• compliments
• memorable observations
• community jokes

Those are not the same thing as unmet viewer needs.

What to do instead

Use likes as:

supporting evidence.

Not as:

your main filter.

A comment with 500 likes deserves inspection.

But so does the unliked comment that expresses the same unresolved problem you have now seen 23 times.

Finding 9: Comments Become Much More Useful When You Have Enough of Them

Across all:

1,954 videos

the median number of classified comments was only:

5.5.

The median video contained:

2 signal-bearing comments.

Even with that sparse coverage:

71.7% of videos

had at least one useful signal.

But the result becomes much stronger when we require more comment depth.

We isolated:

753 videos

with at least:

10 classified comments.

The median video in this group had:

24 comments

and:

8 signal comments.

Among those videos:

• 96.4% had at least one actionable signal
• 84.2% had at least one how-to question
• 77.8% had at least one pain point
• 52.2% had at least one content request

This does not mean:

Reading 10 comments guarantees a great video idea.

The corpus itself is selected.

But it does suggest a practical principle:

Comment research becomes far more useful when you analyze a body of comments rather than waiting for one brilliant request.

How Many YouTube Comments Should You Analyze?

There is no universal magic number.

Our data suggests three useful levels.

1 to 9 comments

Good for:

• quick qualitative clues
• specific questions
• obvious feedback

Weak for:

• claiming a theme is common
• ranking audience demand

10 to 30 comments

Good for:

• finding multiple signal types
• beginning to see repeated problems
• producing an initial opportunity list

Among videos with at least 10 classified comments in our dataset, useful signals were extremely common.

30+ comments

Better for:

• clustering repeated questions
• separating isolated opinions from recurring needs
• comparing subtopics
• prioritizing opportunities

The real goal is not:

hit a comment quota.

It is:

reach enough evidence that the idea is no longer resting on one person's opinion.

Finding 10: The Biggest Audience Need Was Not "How Do I Get More Views?"

Because the corpus focused heavily on YouTube creators, we could also examine what general areas the signal comments concerned.

Among the:

9,677 signal-bearing comments

the topic distribution was:

These percentages describe this specific creator-focused dataset.

They are not universal YouTube audience benchmarks.

But there is still an interesting strategic lesson.

The audience was not only asking:

How do I grow?

A huge amount of demand concerned:

execution.

How to edit.

How to produce.

How to monetize.

How to make workflows work.

How to deal with platform constraints.

How to automate.

That matters because creators often over-focus their content calendars on broad aspiration.

The comments frequently reveal much more concrete friction.

The Seven Comment Signals You Should Actually Track

A useful comment-research system can be much simpler than most AI reports.

Track these seven things.

1. How-To Questions

Look for:

How do I...

How can I...

What should I do if...

Translate into:

tutorials and workflows.

2. Pain Points

Look for:

I keep struggling with...

This never works for me...

My problem is...

Translate into:

diagnostic content.

3. Objections

Look for:

But what if...

This does not work when...

I cannot do that because...

Translate into:

myth busting, alternatives, constraint-specific content.

4. Content Requests

Look for:

Make a video about...

Can you cover...

Please do part two...

Translate into:

direct audience-requested topics.

5. Missing Capabilities

Look for:

I wish I could...

There is no way to...

I need something that...

Translate into:

workflow, product, or process gaps.

6. Tool Requests

Look for:

What tool did you use?

What software should I use?

Translate into:

tool tutorials and buyer-intent content.

7. Comparison Requests

Look for:

Which is better?

A or B?

Is this better than...?

Translate into:

decision content.

That seven-part taxonomy captures far more opportunity than a simple:

questions vs non-questions

filter.

The Comment-to-Video Transformation

The comment should rarely become the title unchanged.

Instead, move through four levels.

Level 1: Raw comment need

Hypothetical signal:

I understand how to find competitors, but how do I know which competitors are actually worth copying ideas from?

Level 2: Underlying problem

The viewer does not have a:

competitor-selection framework.

Level 3: Research question

Which competitor characteristics make their successful videos more strategically useful?

Level 4: Video concept

Which YouTube Competitors Should You Actually Study?

The comment supplies the pain.

The creator supplies:

• research
• evidence
• framing
• originality
• packaging

That is the difference between:

listening to your audience

and:

letting your audience write your channel.

Do Not Build a Video From One Comment

One comment is:

a clue.

Repeated independent comments are:

a pattern.

A pattern appearing under several different videos is:

stronger evidence.

A pattern that also appears under competitors' videos is:

market evidence.

A pattern supported by videos already outperforming is:

much stronger evidence.

Use this evidence ladder.

The goal is to move upward before committing expensive production resources.

Why Competitor Comments Can Be More Valuable Than Your Own

Your comments tell you:

what your current audience wants.

Competitor comments can tell you:

what the market still has not solved.

That distinction is powerful.

Imagine a competitor has a 2-million-view tutorial.

The video proves:

the broad subject has demand.

Now the comments repeatedly reveal:

• one missing step
• one unresolved objection
• one outdated tool
• one beginner problem
• one comparison the creator never covered

That gives you:

proven demand + unsatisfied demand.

That is a stronger starting point than either signal alone.

But do not copy the competitor's content.

Use the comments to identify:

the remaining problem.

Then build your own answer.

The Weakest Strong Video Method

One of the best comment-research workflows is not to analyze random videos.

Find a strong video that appears to have:

an incomplete answer.

The ideal source can have:

• high views
• strong topic demand
• many comments
• repeated audience questions
• missing details
• outdated information
• unresolved objections

That video has already demonstrated audience interest.

The comment section shows where the answer still leaks.

You are researching:

what demand remains after a winner already exists.

That is a real content gap.

Do Not Confuse Comment Volume With Demand Quality

A video with 5,000 comments can be strategically useless.

The comments might consist mostly of:

• jokes
• arguments
• reactions
• fandom
• compliments
• politics
• spam

A video with 80 comments can contain ten extremely specific unresolved problems.

Raw comment count tells you:

conversation volume.

Signal classification tells you:

what the conversation contains.

That is why a comment analyzer should not stop at:

This video has lots of engagement.

The harder question is:

What are viewers still trying to accomplish?

Why Sentiment Is Not Enough

Comment-analysis tools often lead with:

• positive
• neutral
• negative

That can be useful for brand monitoring.

It is much less useful for deciding what video to make.

Consider:

Amazing video, but how would this work for a channel with only 500 subscribers?

Sentiment:

positive.

Strategic signal:

how-to question.

Or:

This strategy is useless if you cannot afford the software.

Sentiment:

negative.

Strategic signal:

objection and constraint.

The idea is hiding in:

intent.

Not mood.

For content research, classify what the viewer needs before classifying how they feel.

Why Sorting by Likes Can Mislead You

The data gives us a particularly useful warning.

The most strategic comment may have:

zero likes.

That is not unusual.

A comment can be extremely valuable while being:

• specific to a difficult use case
• written late
• buried in the thread
• less funny
• less emotional
• less socially rewarding to like

Meanwhile, a joke can receive 2,000 likes.

The joke won the comment section.

The question may still win your next video.

Use likes to answer:

Which comments resonated socially?

Use clustering to answer:

Which problems recur?

Those are different research questions.

The Comment Demand Matrix

Rank every potential idea across two dimensions.

Dimension 1: Recurrence

How often does the underlying need appear?

• once
• several times
• repeatedly
• across multiple videos
• across multiple channels

Dimension 2: Strategic Evidence

How much external evidence supports the topic?

• no proof
• existing normal videos
• one strong performer
• several independent strong performers
• current breakout activity

That creates four useful zones.

Comments answer:

Does the audience have a problem?

Performance research answers:

Does enough demand exist to justify solving it in a video?

You need both.

A 15-Minute YouTube Comment Research Workflow

You do not need an enormous research department.

Step 1: Choose the Right Source Video

Prefer:

• your own high-performing videos
• competitor outliers
• current breakout videos
• videos directly related to your next content pillar

Step 2: Gather Enough Comments

Do not stop after the first three.

Try to review at least:

10 to 30

when available.

Larger comment sections become more useful when you can classify them automatically.

Step 3: Ignore Praise During the First Pass

Not because praise is useless.

Because you are currently searching for:

unresolved demand.

Tag:

• HOW_TO
• PAIN
• OBJECTION
• REQUEST
• MISSING
• TOOL
• COMPARISON

Step 4: Normalize the Problem

These comments:

What about beginners?

Can someone new do this?

Does this work with zero subscribers?

may all describe the same underlying need:

beginner applicability.

Count the need.

Not the exact sentence.

Step 5: Preserve Representative Audience Language

Do not copy comments into your title.

But record phrases viewers naturally use to describe the problem.

That language can improve:

• topic framing
• titles
• hooks
• explanations
• FAQs

Step 6: Validate Outside the Comments

Look for:

• relevant breakout videos
• independent channels
• recent velocity
• recurring successful topics

One comment cluster becomes much more powerful when the wider market also confirms it.

Step 7: Create the Original Angle

Do not make:

competitor video, but with the missing paragraph added.

Create a video with its own:

• question
• thesis
• research
• structure
• examples
• package

How to Prioritize 20 Comment-Derived Ideas

Suppose you collect 20 potential video ideas.

Do not choose the one with the comment that has the most likes.

Rank each opportunity on:

Recurrence

How many independent viewers express the need?

Source Diversity

Does it appear under one video or several?

Specificity

Can you clearly state the unresolved problem?

Proven Demand

Have videos about the broader topic already performed?

Freshness

Is the problem active now?

Channel Fit

Would your existing viewer care?

Information Gain

Can you add something meaningfully better?

A strong comment opportunity is:

repeated + specific + externally validated + relevant + answerable

That is far stronger than:

somebody asked for it.

How to Use Your Own YouTube Comments

Your channel's comments have one major advantage.

You know much more context.

You can connect the comment with:

• viewer behavior
• video performance
• audience retention
• traffic sources
• returning viewers
• subscriber growth

That lets you ask:

Which questions appear underneath videos that attract the kind of audience I want more of?

That is better than treating every commenter equally.

Your highest-view video may have a huge comment section from people who never return.

A smaller video may attract exactly the audience your channel is built for.

Comment research should serve:

channel strategy.

Not raw volume.

How to Use Competitor YouTube Comments

Competitor research requires more caution because you do not have their private analytics.

Start by validating the source video itself.

Use the OverseerOS YouTube Channel Analyzer to understand whether the video sits inside:

• a strong channel
• an unusual breakout
• a repeatable topic pattern
• a historical winner
• an active content cluster

Then analyze the public comments.

If you need a collection or analysis workflow, the guide to the best YouTube comment analyzer tools covers current options for exporting, searching, grouping, and analyzing public comments.

Once an idea is validated, move the opportunity into the OverseerOS Content Planner with the evidence behind it.

The workflow should be:

comment signal -> market validation -> original angle -> content plan

not:

comment -> instant AI script.

What YouTube Comments Are Best At

Comments are particularly good at revealing:

Missing Depth

You explained what. I still need how.

Missing Audience Segment

What about beginners?

Missing Constraint

What if I do not have a budget?

Missing Comparison

Which option is actually better?

Missing Implementation

What do I do first?

Missing Update

Does this still work?

Missing Consequence

What happens if this goes wrong?

Missing Tool

What should I use?

Every one is a type of:

information gap.

That is why comment analysis works so naturally for content research.

What YouTube Comments Are Bad At

Comments are not perfect market research.

The commenter population is self-selected.

People who write comments may be:

• more engaged
• more frustrated
• more enthusiastic
• more opinionated
• more experienced
• less experienced

than the silent majority.

Comments are also influenced by:

• the original video
• the creator's framing
• other comments
• community culture
• current events

So do not say:

15 people asked this, therefore 15% of my entire audience wants it.

That inference is not supported.

Use comments as:

qualitative demand evidence.

Not as:

a representative survey.

The Biggest Comment-Mining Mistakes

Mistake 1: Reading Only Top Comments

Highly liked comments are not automatically the most strategic.

Our data did not show useful comments attracting more likes.

Mistake 2: Looking Only for Direct Requests

Direct content requests represented only:

13.5% of signal-bearing comments.

Questions and pain points were much larger.

Mistake 3: Treating One Comment as Validation

One viewer is one viewer.

Look for recurrence.

Mistake 4: Copying Audience Wording Too Literally

Use viewer language to understand the need.

Do not turn comments into copied titles or scripts.

Mistake 5: Ignoring Objections

An objection can reveal a more valuable topic than a request.

Mistake 6: Ignoring the Source Video

A comment under a dead video does not carry the same market evidence as the same comment under several current outliers.

Mistake 7: Letting AI Invent the Pattern

Always keep the source comments available.

The model should help classify evidence.

It should not fabricate consensus.

A Comment-to-Content Brief Template

Use this before producing a comment-derived idea.

Source video:
Source channel:
Why this video is worth researching:

Comment signal type:
Repeated viewer need:
Number of independent examples:
Number of source videos:
Number of source channels:

What viewers appear to be struggling with:
What the existing video already answered:
What remains unanswered:

External demand evidence:
Relevant competitor winners:
Current momentum:

My original angle:
Why this angle adds information:

Possible title:
Thumbnail promise:
Hook:
Key payoff:

Decision:
GO / RESEARCH MORE / SKIP

The most important field is:

What remains unanswered?

That is where the new video earns its existence.

The Strongest Comment Idea Is Usually a Gap, Not a Request

Imagine an existing video covers:

How to Start a YouTube Channel

The comment section contains repeated questions about:

• choosing the first topic
• finding competitors
• picking a niche
• getting initial views
• knowing whether an idea is good

The obvious reaction is:

Make five beginner tutorials.

A stronger analysis asks:

What larger problem connects these questions?

Possibly:

Beginners do not have a system for validating what to make before publishing.

Now you have a more differentiated concept:

How to Know What Your First YouTube Videos Should Be Before You Waste a Month

You have moved from:

comment extraction

to:

audience intelligence.

That is the level that matters.

Why This Can Beat Keyword-Only Research

Keyword research captures:

what people type into search.

Comments capture:

what viewers still need after consuming an answer.

Those are not the same dataset.

Suppose thousands of people search:

how to edit YouTube videos

The videos answer the basic question.

But the comments repeatedly say:

How do I edit this style without spending six hours per video?

The search query gives you the market.

The comment gives you the:

specific friction.

That is where differentiated video ideas often come from.

The strongest workflow combines:

search demand + successful videos + comment gaps.

The Hidden Opportunity: Questions After Successful Videos

A question underneath a weak video tells you:

someone wants an answer.

A repeated question underneath a highly successful video tells you something stronger:

a proven audience consumed a popular answer and still had this unresolved need.

That is an excellent research target.

This is why comments should not be analyzed separately from video performance.

The source matters.

A content gap is more credible when it sits adjacent to:

demonstrated demand.

Final Verdict

How should you find YouTube video ideas from comments?

Do not wait for viewers to tell you exactly what to make.

OverseerOS analyzed:

27,555 public comments across 1,954 videos and 44 channels.

We found:

9,677 comments, or 35.1%, with an actionable audience-demand signal.

But only:

1,302

were direct content requests.

The largest signals were:

• 3,333 how-to questions
• 2,665 pain points
• 1,637 objections

Together, questions and pain points appeared in:

60.6% of signal-bearing comments.

The data also showed why sorting comments by popularity is not enough.

Signal comments were slightly less likely to receive likes than comments without a classified signal.

And once we restricted the analysis to videos with at least 10 classified comments:

96.4% contained at least one actionable signal.

So the best way to mine YouTube comments is not:

Find the most-liked request and make that video.

It is:

Collect enough comments to see recurring needs, classify the type of problem, group similar needs across videos, then validate the strongest pattern against real market performance.

Comments are not your content calendar.

They are one of the best places to discover:

what the existing content still failed to solve.

That is much more valuable.

Frequently Asked Questions

How do I find YouTube video ideas from comments?

Collect comments from your own videos or relevant competitor videos, classify them into recurring questions, pain points, objections, requests, tool needs, and comparisons, then validate the strongest patterns against actual video performance before producing.

How many YouTube comments should I analyze?

There is no universal minimum. In the OverseerOS dataset, 753 videos had at least 10 classified comments, and 96.4% of those videos contained at least one actionable signal. More comments make recurring patterns easier to distinguish from one-off opinions.

What types of YouTube comments produce the best video ideas?

How-to questions and pain points were the largest categories in this study. Together, they appeared in 60.6% of all signal-bearing comments.

Should I look for people asking me to make a video?

Yes, but do not stop there. Direct content requests represented only 13.5% of actionable comments in this study.

Are questions in YouTube comments good video ideas?

Questions are excellent research signals. We identified 3,333 how-to questions, making them the largest signal category. One question is a clue. Repeated similar questions are much stronger evidence.

Should I sort YouTube comments by likes to find video ideas?

Not by likes alone. Signal-bearing comments were slightly less likely to have likes than other comments in this dataset. Use likes as supporting evidence, not your primary research filter.

Are highly liked comments more valuable for content research?

Not necessarily. Among the most-liked comments in this dataset, actionable signals were slightly less common than across the comment corpus overall.

Can YouTube comments reveal content gaps?

Yes. Comments can reveal questions, frustrations, objections, missing capabilities, and decisions that the existing video did not fully resolve.

Should I analyze competitor comments?

Yes, especially under proven or breakout videos. Competitor comments can reveal unresolved demand inside an audience that has already demonstrated interest in the broader topic.

Is it okay to use competitor comments for video ideas?

Use public comments as aggregate research evidence, not as content to copy. Extract the underlying problem, validate it independently, and create your own original answer.

What is the best comment signal for a tutorial?

How-to questions are the clearest tutorial signal because the viewer is explicitly trying to accomplish a task.

What is the best comment signal for a problem-solving video?

Pain points and objections are especially useful because they reveal where an existing method fails or where the viewer gets stuck.

What is the best comment signal for a comparison video?

Look for viewers actively choosing between tools, methods, products, strategies, or workflows. Comparison requests were less frequent in the study but can carry strong decision intent.

Do YouTube comments represent the whole audience?

No. Commenters are a self-selected group of viewers. Treat comments as qualitative evidence rather than a representative survey of everyone who watched.

Can AI analyze YouTube comments for video ideas?

Yes. AI can help classify large comment sets by intent and topic, but the strongest themes should still be checked against the original comments and validated with performance evidence.

Should I use sentiment analysis for YouTube content ideas?

Sentiment can help, but intent is usually more actionable. A positive comment can contain a valuable question, while a negative comment can reveal an objection or content gap.

How do I turn a viewer question into a YouTube title?

Do not copy the question automatically. Identify the underlying viewer problem, research the strongest answer, decide your original angle, and then package the resulting promise into a title.

How do I know if a comment-derived idea has enough demand?

Look for recurrence across independent comments, multiple videos, or multiple channels. Then check whether related videos have already demonstrated strong audience demand.

Are comments better than keyword research?

They answer different questions. Keywords reveal what people search for. Comments reveal what viewers still want to know after watching existing content. Using both is stronger than relying on either alone.

What is the biggest mistake when mining YouTube comments?

Looking only for direct requests. In this study, questions, pain points, and objections represented far more audience-demand signals than explicit content requests.