Comment analysis for brands: beyond positive and negative.
A sentiment score tells you whether a comment sounds happy or angry. It does not tell you who is commenting, what they want, or whether they are talking about your product at all. Here is how comment analysis works when the model understands the video.
The short answer.
Comment analysis classifies what people write under your content: the intent behind it, whether the person is an advocate, an existing customer or a potential customer, whether it is sarcastic, whether it is relevant, and whether it is spam, toxic or hate speech. Noodle4’s classification model uses the video as context, so it can also find comments about your brand or product issues and praise even when the comment does not mention them.
Last updated 14 October 2026.
Why sentiment scores are not enough
Most comment tools give each comment a score: positive, neutral or negative. It is a quick summary and it hides most of what matters.
- Sarcasm flips the score. “Love how it broke on day two” reads as positive to a word-based model.
- Negative is not always a problem for you. A comment complaining about a rival, or about the creator, is negative but not about your brand.
- Positive is not always useful. “Great video!” says nothing about your product.
- It ignores who is writing. A comment from a loyal customer and one from someone who has never bought look the same.
The questions teams actually ask are different. Is this about us? Is there a problem to fix? Is this person about to buy? Should we reply? A score cannot answer those.
What comment analysis can classify
| Classification | What it tells you | Example comment |
|---|---|---|
| Intent | What the person wants: to buy, to ask, to complain, to recommend | “Does this work on curly hair?” |
| Advocate, existing or potential customer | Who the commenter is in relation to your brand | “Been using this for a year, never switching” |
| Sarcasm | Whether the words mean the opposite of what they say | “Great, another bottle that leaks” |
| Relevance | Whether the comment is about your brand, product or campaign | “This is the one I use for my fringe” |
| Positive and negative, in context | Sentiment that is judged against the video, not only the words | “That’s exactly what happened to me” |
| Spam | Promotions, scams, bots and repeated posts | “Check my page for free followers” |
| Toxicity and hate speech | Abuse aimed at people or groups | Insults and slurs |
| Brand and product issues or praise | Problems and praise about your brand or product, even if it is not named | “The pump stopped working after a week” |
Example comments are invented for illustration. The classes shown are those Noodle4 applies.
Why the video matters
A comment is rarely a complete thought. It is a reply to something on screen. “Same here” means nothing alone, but under a video of someone reviewing a leaking bottle, it is a product complaint.
Most comment tools read each comment as isolated text. Noodle4’s classification model uses context from the video as well, so it knows what the comment is responding to. That is what allows it to judge sarcasm, relevance and intent more accurately, and to find feedback that does not name the brand.
Product feedback that never names the product
Search for your brand name in comments and you will find only a fraction of the conversation. People talking about a product in a video rarely repeat its name.
- “Mine did the same thing.” A product issue, under a video that shows a faulty pump.
- “Where can I get that?” A purchase signal from a potential customer, under a video that shows your product.
- “This is the only one that works for me.” Praise, from someone who is probably already a customer.
Keyword monitoring misses all three. Because the model knows what the video shows, it can link each comment to the right brand or product and classify it as an issue or as praise.
Who is commenting: advocates and customers
The same words carry different weight depending on who writes them. A comment from an advocate defending your product is worth keeping visible and perhaps thanking. A comment from a potential customer asking a question is a chance to reply. A comment from an existing customer reporting a fault is a support case.
Classifying the commenter as an advocate, an existing customer or a potential customer lets your team route comments, not just count them. It also changes how you read the totals: a rise in negative comments from potential customers points to a different problem than one from existing customers.
Sarcasm
Sarcasm is common in comments and costly to get wrong. A model that reads the words alone will count “Amazing, it fell apart in a week” as praise, and your report will show happy customers while the real conversation is a complaint.
Using the video as context helps. If the video shows the product failing, the same sentence is clearly sarcastic. Noodle4 classifies sarcasm so it is not counted as positive. No model is perfect on sarcasm, so test it on your own comments in a pilot.
Spam, toxicity and hate speech, without hiding real criticism
Moderation tools that filter on negativity make a common mistake: they hide complaints along with abuse. A comment saying your product caused a rash is negative, but it is not toxic and you need to see it.
Noodle4 classifies spam, toxicity and hate speech separately from negative sentiment about your product. You can filter out abuse and scams while keeping genuine criticism visible, and a reviewer can check the evidence for each classification.
What teams do with the results
| Team | What they do |
|---|---|
| Customer support | Route product issues and questions to the right person, even when the brand is not named |
| Product | Track recurring issues and praise by product and over time |
| Social and community | Reply to potential customers, thank advocates and hide spam |
| Brand and PR | Spot a spike in issues or abuse early, and see who is driving it |
| Influencer and campaign teams | See how an audience reacts to each creator, beyond the creator’s own numbers |
Noodle4 results are available in the app, in Slack, through the API and by asking from Claude or other MCP clients, so the data goes where the team already works.
How to run a pilot
- Choose three videos. Include one with sarcasm, one with a product problem and one with a lot of spam or abuse.
- Classify the comments by hand. Have two people label a sample, so you know where people disagree.
- Run the same comments through the model. Compare the labels with the people’s.
- Check the unnamed comments. Look for product feedback that does not mention the brand, and see what the model found.
- Review the disagreements. Decide who was right in each. This shows where the classes match your definitions.
What Noodle4 includes
Noodle4 is a full social intelligence platform, not only a video tool. It reviews text as well as video and images, and it includes:
- Text, video and image review. Reads captions, on-screen text, spoken words, images and comments, so the whole piece of content is understood.
- Taxonomies. Organise content and comments into your own categories, so results match how your team thinks.
- Visual-level SKU tracking. Spot products and SKUs as they appear in videos and images, not only when they are named in text.
- Classifications. Every piece of content and every comment is classified by topic, type and relevance.
- Anomaly detection. Flags unusual spikes and shifts, so you see what is trending before it spreads.
- Slack and Slack Connect. Reviews, risks and reports go to your team’s channels, and to shared channels with partners.
- MCP. Ask about your creators, campaigns and social intelligence from Claude, Cursor or any MCP client.
- API. Send content in and get structured results back from your own systems.
That covers social listening, brand monitoring, influencer screening and monitoring, content review and reporting in one place. It is priced on usage, not seats, from £99 a month.
How we know this
These notes come from building Noodle4’s review and listening pipelines, not from a published academic sample. Cost curves, intent splits, and ASR collisions are from internal R&D and client-shaped tests. They will not match every category. We include them because the failure mode is structural — not because one campaign is universal.
Alex Gray-Johnson is Co-Founder at noodle4, with 8 years in AI classification models and marketing.
Frequently asked questions
What is comment analysis?
It is classifying the comments on social content to understand what people want and think, such as their intent, whether they are customers, and whether they raise issues or praise.
How is it different from sentiment analysis?
Sentiment analysis scores comments as positive, neutral or negative. Comment analysis also classifies intent, who is commenting, sarcasm, relevance and issues, and uses the video as context.
Can it find comments about my product that do not mention it?
Yes. Noodle4’s model uses context from the video, so it can classify comments as brand or product issues or praise even when they do not name the brand or product.
Can it tell if someone is a customer?
Noodle4 classifies commenters as an advocate, an existing customer or a potential customer, based on what they write.
Can it detect sarcasm?
Yes, using the video as context. No model is perfect, so test it on your own comments.
Does it hide negative comments?
No. It separates spam, toxicity and hate speech from genuine criticism, so you can filter abuse and keep complaints visible.
Where do I see the results?
In the Noodle4 app, in Slack, through the API, and by asking from Claude or other MCP clients.