Sentiment and Topic Authority: See How AI Mentions You

In short: WriteWorks has added sentiment and topic authority reporting to its AI-search visibility tracking, so you can see not only whether an AI engine mentions your brand but how it describes you and on which subjects. Every captured mention is classified by tone, and mentions are grouped by the topics they relate to. It's aimed at growth, brand and search-marketing leaders who already track presence and now need to manage framing.

Presence-only measurement hides a real risk. A brand can appear in plenty of AI answers and still lose deals because it is consistently framed as the expensive option, the hard-to-implement option, or the option that suits someone else's use case.

What is sentiment and topic authority tracking?

Sentiment and topic authority tracking is a reporting layer inside WriteWorks that classifies the tone of each AI mention of your brand and groups those mentions by subject, so you can see where you are described favourably and where you are not. Instead of a single mention count, you get tone plus context.

The two views answer different questions. Sentiment answers "how am I being characterised?" Topic authority answers "what am I being characterised as good at?" Read together, they tell you which parts of your positioning the models have actually absorbed.

How sentiment and topic authority work

Both views are built on the mentions WriteWorks already captures when it runs your tracked prompts across supported AI engines. Nothing new needs to be instrumented - the same prompt runs that feed your visibility and citation reporting now also feed tone and topic classification.

• Sentiment classification. Each mention is assessed in the context of the answer it appeared in and labelled as positive, neutral or negative, so you can separate genuine advocacy from a passing name-check.
• Trend over time. Sentiment is recorded per run, which means you can see whether the mix is shifting after a launch, a pricing change, a review-site update or a critical article.
• Breakdown by engine. Tone is not uniform across engines. Because results are stored per engine, you can see where framing differs and prioritise the channel that matters most to your funnel.
• Competitive context. The same classification applies to tracked competitors, so brand and competitor sentiment can be compared side by side rather than read in isolation.
• Topic grouping. Mentions are associated with the topics and themes of the prompts that produced them, which surfaces the subject areas where you appear most often and most favourably.

What it will not do is explain intent. Classification tells you the tone of a mention; the underlying sources are what tell you why. That is where cited sources analysis earns its place in the workflow.

Who this is for

It's especially useful if you:

• Own brand positioning and need evidence of how AI assistants paraphrase your value proposition, not just how often they name you.
• Run competitive programmes and want to know which topics rivals are treated as the default answer for.
• Handle a launch, rebrand or pricing change and need to watch how framing moves in the weeks afterwards.
• Manage several brands or clients and need a defensible way to report quality of visibility alongside volume.
• Work in a category where a single negative framing - cost, complexity, support - repeatedly costs you shortlist places.

Advanced practitioners benefit too. If you already run structured prompt programmes, topic authority gives you a way to decide which clusters deserve more content investment and which are already won.

What this changes

It changes the unit of analysis from the mention to the mention's meaning. A flat month for share of voice can conceal a good month for sentiment, and a rising mention count can conceal a drift towards being described as a niche or legacy choice.

It also changes prioritisation. When topics are ranked by how often and how positively you appear, the gaps become a shortlist: the subjects where buyers ask questions, competitors are cited and you are not. That is a more concrete brief than "publish more".

This matters because AI experiences summarise and characterise sources rather than simply listing them, as set out in Google's documentation on AI features in Search. When a model is doing the summarising, the adjectives it chooses are part of your visibility.

How to use it

1. Open your workspace and go to the AI Search Visibility section for the brand you want to review.

2. Select the sentiment view to see the positive, neutral and negative mix for the current period.

3. Filter by engine to compare how framing differs between the AI engines you track.

4. Add tracked competitors to see relative sentiment rather than an absolute score.

5. Switch to the topic breakdown to see which subjects you are mentioned on most, and how tone varies by subject.

6. For any mention that looks wrong or damaging, open the underlying answer through brand mentions and alerts and work back to the sources behind it.

Tips for a better result

• Judge trends, not single runs. One negative mention is noise; a sustained shift in the mix is a signal.
• Track enough prompts per topic that a topic view is meaningful - a handful of prompts will not represent a category.
• When you find negative framing, check whether the sources behind it are outdated. Correcting a stale third-party page is often faster than publishing something new.
• Pair topic strengths with competitor share of voice before reallocating budget, so you invest where you can realistically win.

Frequently asked questions

How does WriteWorks decide whether a mention is positive or negative?

Each mention is classified in the context of the full AI answer it appeared in, then labelled positive, neutral or negative. Assessing the surrounding answer rather than the brand name alone means a recommendation is distinguished from a neutral listing, and a caveat is distinguished from a straightforward endorsement.

What is the difference between sentiment and topic authority?

Sentiment measures how your brand is characterised - favourably, neutrally or unfavourably. Topic authority measures which subjects you are mentioned on, and how consistently. Sentiment tells you whether the framing helps you; topic authority tells you where that framing is being applied and which subject areas you own.

Can sentiment be compared against competitors?

Yes. The same classification is applied to the competitors you track, so sentiment can be read comparatively across brands and engines. This matters in categories where every vendor is described with similar caveats; relative tone is usually more actionable than an absolute figure.

How can negative framing actually be fixed?

Start with the evidence. Identify the recurring theme, trace it to the sources AI engines are drawing on using citation tracking across AI engines, then correct outdated pages, publish clearer answers to the specific objection, and monitor whether the mix shifts over subsequent runs.

The bottom line

Measuring whether AI engines mention you is table stakes; measuring how they describe you, and on which topics, is what turns AI-search reporting into a plan you can act on. If you'd like to see the tone and topic profile behind your current visibility, open the sentiment view in your workspace - and the AI search visibility metrics KPI guide is a useful companion for deciding which of these numbers belongs in your board reporting.