Best tool for monitoring sentiment in AI answers, compared on which tier the sentiment row sits on

Sentiment in an AI answer is a narrower thing than sentiment in social listening, and the tool question is narrower with it. An assistant speaks with one voice, usually a measured one, so what you are really measuring is whether that voice qualifies you, hedges about you, or recommends you plainly. Read on 31 October 2026, the published positions are clear enough to shop from.

Scrunch publishes sentiment analysis on its 250 dollar Core plan and on Enterprise. Goodie publishes brand sentiment tracking on all three tiers from 295 dollars, and adds two figures most of the market does not name at all: an adverse citation share and an inaccuracy rate. AthenaHQ publishes real time brand sentiment intelligence in its monitoring set. Evertune publishes keyword sentiment on Enterprise only, with dashes against both its priced plans, so its 60 dollar and 800 dollar tiers cannot answer this question.

Which tier carries the sentiment row

Vendor	Sentiment on the entry plan	Published name of the feature
Scrunch	yes, Core at 250 dollars a month	Sentiment Analysis, on Core and Enterprise
Goodie	yes, Core at 295 dollars a month	brand sentiment tracking, plus adverse citation share and inaccuracy rate
AthenaHQ	published in the monitoring set	real time brand sentiment intelligence
Profound	stated in its FAQ rather than a plan row	sentiment, beside citations, ranking and competitive presence
Evertune	no, Enterprise only	Keyword Sentiment
Brandwatch	its native strength, no price published	sentiment over published conversation
Conductor	no sentiment row on the pricing page	not published
AI Knows Us, ours	no, we publish no sentiment metric	we store the answer text instead

Our row is the honest one again. We publish no sentiment score at any tier. What our plans publish is the answer itself, which is the input a sentiment label is computed from, and for many buyers reading twenty answers is more use than a number. If a tracked sentiment trend is a requirement, four vendors above publish one and two of them do it on their cheapest plan.

The three questions that separate a sentiment number from a sentiment label

1. How many readings is each label computed from? One reading per prompt gives you one opinion from a system that does not answer the same way twice. Evertune publishes 100 samples a prompt from its Pro plan, which is the only published sample count on this roster, and its sentiment feature sits a tier above that.
2. Is the full answer text stored? A label is unarguable and useless. The sentence that says your product is probably fine for smaller teams is the one your positioning has to answer, and you can only act on it if you can read it.
3. Does the classifier agree with a human? Take 50 stored answers, label them yourself, and compare. If the tool and the person disagree on more than a handful, the trend line is measuring the classifier. The three figures worth reporting and how to compute them properly are set out in brand sentiment analysis, what to measure and how to compute it.

Why hedging matters more than negativity here

Assistants rarely say anything rude about a company. What they do is qualify: a safe choice for simple needs, more suited to small teams, less established than the alternatives. Those sentences classify as neutral in most schemes and they cost deals, because a buyer reads them as a reason to pick somebody else.

So the useful cut is not positive against negative. It is recommended plainly, recommended with a qualifier, or mentioned without a recommendation, and you can produce that from stored answer text in a spreadsheet. Goodie's adverse citation share is the nearest published metric to it, which is a reason to ask that vendor what counts as adverse and against what denominator.

Where the negative sentence usually comes from

An assistant's tone about you is downstream of its sources. A single critical thread, an old review, a comparison page written by a competitor: any of those can set the qualifier that keeps turning up. That is why a sentiment product without a source list underneath each answer leaves you with a mood and no next action, and why the first thing to do with a negative reading is to open the citations. The case where an assistant cites a Reddit thread about you that is wrong works through exactly that trace, including what can and cannot be done about the thread itself.

Where rivals beat us on this job, said plainly

All four of the vendors with a published sentiment feature beat us on it, because we do not have one. Scrunch and Goodie do it on their entry plans. Brandwatch has been computing sentiment at scale over public conversation for years and remains the strongest choice when your question spans published media as well as assistant answers, and it is the one vendor here that cannot be priced from its own page. Evertune's sampling design is the right foundation for a sentiment figure even though the feature itself sits on Enterprise.

Our contribution to this job is the raw material and the discipline. We store what each app said, on five named apps, with the date and the sources, and we report cited and named as separate columns rather than blending them into one score. A sentiment number computed from stored answers you can read is a finding. The same number arriving without the answers is a mood ring.

Questions about sentiment in AI answers

Which tool tracks sentiment in ChatGPT answers on the cheapest plan?

On published rows read 31 October 2026, Scrunch at 250 dollars a month and Goodie at 295 both include sentiment on their entry tier. Evertune's sentiment feature is Enterprise only.

Is AI answer sentiment the same as social listening sentiment?

No. Social listening classifies many voices and finds outrage. Assistant sentiment classifies one voice that is rarely rude, so the signal lives in qualifiers rather than in insults.

Can I measure this without a tool?

Yes, and it is a good first week. Ask twenty buying questions, store the answers, and sort each into recommended plainly, recommended with a qualifier, or not recommended. The qualifiers will repeat, and the repeats are your positioning list.

How many answers before a sentiment trend is real?

Enough that one answer changing cannot move it visibly, and sampled often enough that you are watching the system rather than one roll of the dice. Ask any vendor for its readings per prompt before you accept a trend line.

What is the first fix when the sentiment is consistently qualified?

Answer the qualifier in public, on your own site, in the words the assistant used. If it says you are less established, publish the dated evidence that settles it. The engines read your pages, so the sentence that argues back has to exist somewhere they can find it.

Buy the label, keep the sentences

A sentiment feature is worth having on a plan you were buying anyway, and two vendors include it at their entry price, which makes this a weak reason to upgrade and a fine reason to prefer one shortlist entry over another. What decides whether the number ever changes anything is the material underneath it: the stored answers, the sources beside them, and somebody willing to read fifty of them and write down the qualifier that keeps appearing. That reading is unglamorous, it is where the positioning work actually happens, and no dashboard will do it for you.

How we know this

We read the assistants rather than guessing what they do. In our own audit of 1,260 saved answers from ChatGPT and Gemini across the United States, the United Kingdom and India, 82.0% of every citation landed on a site the business itself owned, and review directories took 0.7%. Counted as one domain per reading, 16,516 of 20,143.

That is the whole asymmetry: the pages you own are the pages an assistant quotes back to a buyer. See the dated readings and the method on our proof page.

Find out whether assistants name your business, on your own domain and your own buying questions.

By Aditya Goel, Founder, AI Knows Us. Reviewed 1 November 2026.