What You’ll Find in this Article…

Every major AI platform – ChatGPT, Claude, Gemini, Grok and Perplexity – has already formed a judgment about your business. It has assigned your brand an invisible and previously unknown Authority Score that measures how much it trusts your expertise, and a Sentiment Score that reflects how the world’s online conversation about your company reads to the model. Nobody told you it was happening, nobody sent you a report and there no dashboard where you can check the numbers (at least not yet). But those two scores are actively determining whether AI recommends your business or steers potential customers to your competitors right now.

In the article you will discover exactly what Authority and Sentiment Scores are and why both matter independently. Also –

• How LLMs construct these evaluations from real signals across the Internet
• Which five specific authority signals carry the most weight with the Big 5 platoforms
• Which five sentiment sources are shaping your brand’s AI reputation
• The four score combinations and what each one means for your competitive position
• The most common score killers that businesses unknowingly commit every day
• A dual-track action plan for moving both numbers decisively in your favor

By the end of this article, you will understand how LLMs determine your Authority and Sentiment Scores well enough to start managing them.

Right now, every major AI platform has formed an opinion about your business. It has assigned your brand a level of Authority – How much it trusts what you say – And it has determined a sentiment position – whether the overall signal around your name and the brand’s name leans positive, neutral, or negative. You did not fill out a form. Nobody told you it was happening. But those two invisible scores are shaping whether AI recommends you or someone else, every single day.

There is currently no dashboard where you can log on and check your aggregate AI Authority Score. There is no analytics panel displaying your AI Sentiment Rating. These evaluations happen inside each LLM – woven into billions of parameters trained on everything the Internet has ever said about your industry, your competitors, and your brand specifically.

But just because they are invisible does not mean they are unmanageable. The signals that feed those evaluations are entirely real, entirely external, and  – with the right strategy – entirely within your control.

What Are Authority & Sentiment Scores Really?

Before we talk about how to influence these evaluations, it is important to understand what they actually are and what they are not.

Authority Score – The AI’s Trust Measurement

An AI Authority Score is not a single number sitting in the database somewhere. It is a composite judgment the model makes about how credible, knowledgeable, and trustworthy your brand is on a given subject. It is built from the cumulative weight of every signal the model encountered during training and retrieval – how often your brand appears, in what contexts, alongside what other entities, and with what level of corroboration from independent sources.

Think of it as the AI’s answer to this question: “If I recommend this company, how confident am I that the recommendation will be correct and trustworthy?” High authority means high confidence. Low authority means that AI looks elsewhere.

Sentiment Score – The AI’s Perception Measurement

An AI Sentiment Score reflects the overall emotional and evaluative tone of everything the model has processed about your brand. It aggregates reviews, social mentions, new coverage, forum discussions, customer complaints, industry commentary, and more – and derives a directional signal: is the conversation around this brand predominantly positive, mixed, or negative?

Sentiment matters because AI systems are not just trying to find the most authoritative source – they are trying to make a recommendation that will serve the user well. A highly authoritative brand with a deeply negative sentiment profile creates a contradiction that the AI resolves by hedging its recommendation or choosing a safer alternative.

AUTHORITY SCORE	SENTIMENT SCORE
Measures credibility and topical expertise	Measures perceived reputation and brand experience
Built through consistent expert content and citations	Built through reviews, mentions, and public conversations
Signals: How often and where AI encounters your brand	Signals: What tone surrounds your brand across the web
Damaged by: Thin content, inconsistency, no external coverage	Damaged by: Negative reviews, complaints, controversies
Improved by: Deep expert content and third-party validation	Improved by: Active review strategy and reputation management

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How LLMs Build Your Authority Score

Authority is not declared. It is inferred. The AI does not take your word for it when your website says, “We are the industry leaders in…” It looks at what the rest of the Internet says about you – independently, consistently, and over time. Here is how that inference actually works.

SIGNAL 1 – Training Data Frequency & Context

Every LLM was trained on an enormous corpus of Internet content – Web pages, articles, books, forums, databases, and more. The more your brand appears in that training data, the more the model has been exposed to your name, your expertise, and your context. But raw frequency is not enough. The context matters enormously.

A company mentioned dozens of times in complaint threads has a high frequency but damaging context. A company mentioned consistently in trade publications, technical forums, and expert roundups, and peer-reviewed content has both frequency and authoritative context – and the model weighs these very differently.

WHAT THIS MEANS FOR YOU: Your brand needs to appear regularly in high-quality, expert-level contexts across the Internet, not just on your own website. Third-party mentions in trade media, citations in industry guides, expert commentary in professional publications, all these are what build training-data authority.

SIGNAL 2 –  Topical Depth and Specificity 

LLMs evaluate authority not just at the brand level but at the topic level. You can have high authority on one subject and zero authority on another. And the models somehow know the difference. A company with fifty deep, specific, technically detailed articles on industrial coating applications has measurably higher topical authority on that subject than a company whose website has three generic paragraphs about ‘our coating solutions’.

The specificity signal is critical. Broad, vague content is processed by the AI as low-confidence information. Specific, detailed, technically accurate content – naming specific conditions, citing specific performance data, addressing specific edge cases – signals genuine expertise to the models.

CONTENT DEPTH MATTERS: Every product or service page on your site should be able to answer the ten most specific questions an expert in your field would ask about it. If a page cannot do that, the AI treats it as thin content and discounts its authority contribution.

SIGNAL 3 – Citation and Cross-Reference Density

In academic publishing, the number and quality of citations a paper receives is the primary measure of it influence and authority. LLMs apply a structurally similar logic to web content. When multiple independent, credible sources reference your brand, link to your content, or cite your expertise, the model interprets that cross-reference network as strong authority confirmation.

This is why a company that has never been covered in a trade publication, never been cited by an industry association, and never appeared in a third-party review or comparison article has a significantly lower AI authority score than a competitor with even modest independent coverage, regardless of how polished their website is.

THE CITATION PRINCIPLE: AI evaluates authority the same way academia does; not by what you say about yourself, but by how many credible, independent sources say it for you. One strong trade press mention can move your authority score more than ten new pages on your own website.

SIGNAL 4 – Entity Consistency & Clarity

LLMs build a mental model of your brand as an entity – a specific, real-world thing with defined attributes. When your company name, your product descriptions, your industry category, and your geographic presence are described consisitently across dozens of web sources, the models have a clear, confident entity definition to work with. When those descriptions are inconsistent or contradictory, the models’ confidence in your entity fails and lower entity confidence directly reduces authority.

SIGNAL 5 – E E A T Signals (Google’s Framework Applied Broadly)

Google formalized the concept of Experience, Expertise, Authoritativeness, and Trustworthiness or E=E-A-T. A framework for evaluating content quality. While Google’s framework is explicitly for its own ranking systems, the underlying signals of real author credentials documented experience, verifiable expertise, and independently confirmed trustworthiness are exactly what LLMs process when building authority assessments.

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If Authority is about what the AI believes you know, Sentiment is about what the AI holds that people feel about you. And the breadth of sources that feed a sentiment assessment is far wider, and far harder to control, than most businesses realize.

SENTIMENT SOURCE 1 – Customer Reviews Across All Platforms

Reviews are the single most direct and high-volume sentiment signal that LLMs process. Google Reviews, Yelp, industry-specific platforms, Amazon, Trustpilot, G2, Capterra, and dozens of other review aggregators all contribute to the sentiment picture the model builds about your brand.

Critically, LLMs do not just read the star ratings. They process the actual language of reviews – the specific words customers use to describe their experience, the complaints that appear repeatedly, the praise that recurs across independent reviewers.  A pattern of reviews mentioning ‘slow response times’ or ‘poor installation support’ becomes a sentiment tag the models associate with your brand, and that tag persists in every recommendation context.

THE LANGUAGE SIGNAL: The specific vocabulary your reviewers use follows your brand into AI recommendations. If then reviewers use a phrase, “difficult to work with,” that phrase becomes a sentiment association for your entity in the model’s knowledge base. This is why proactive review management is not optional, it is a reputation (sentiment) infrastructure.

SENTIMENT SOURCE 2 – Social Media Conversation

Every mention of your brand on social platforms, from LinkedIn endorsements to Twitter complaints to Facebook reviews to Reddit threads, contributes to the sentiment signal LLMs process. For platforms like Grok that have direc real-time access to X, this is immediate. For other models, social content feeds into training data and retrieval corpora over time.

The valence of social conversation compounds. A single negative viral thread can create a persistent negative sentiment spike. Consistent positive social engagement builds a steady positive baseline. The model does not weigh these linearly – highly visible negative content (a viral complaint, a widely shared negative review) receives disproportionate weight because its frequency in the training corpus is amplified by the sharing.

SOCIAL SENTIMENT IS NOT SOFT: A viral complaint or a negative Reddit thread about your company can embed a negative sentiment signal in an LLM’s understanding of your brand for years. Because that content gets crawled, indexed, cited, and included in training data. Fast, public, professional responses to negative social content are not PR courtesy – they are AI reputation defense.

SENTIMENT SOURCE 3 – News & Media Coverage

Coverage in news publications, trade journals, industry blogs, and media outlets is among the highest-weighted sentiment sources LLMs process, because news content is typically produced by organizations with explicit editorial standards, which the models treat as a credibility multiplier.

Positive coverage – product launches, contract wins, industry awards, executive profiles, and innovation stories – feeds strong positive sentiment signals. Negative coverage e.g., product recalls, legal disputes, regulatory actions, and customer complaints, elevated to the media, creates powerful and durable negative sentiment tags that can follow a brand for years in the model’s knowledge base.

THE DURABILITY PROBLEM: News content is among the most-cited, most-indexed, and most persistent content types on the Internet. A negative news story from 2019 may still be a significant component of an LLM’s sentiment assessment of your brand in 2026. This is why a proactive earned media strategy – getting positive stories published consistently is the only reliable counter to negative historical coverage

SENTIMENT SOURCE 4 – Forum & Community Discussions

Industry forums, Reddit communities, Quora threads, LinkedIn comment sections, and professional community platforms are rich with unfiltered opinion and LLMs process them as real-world social proof of sentiment. Unlike polished marketing content, forum discussions represent what practitioners actually think about brands when there is no audience filter.

When contractors discuss which manufacturer has the best technical support in an industry forum, when engineers debate product quality in a professional subreddit, when buyers share vendor experiences in a procurement community – those conversations create direct sentiment signals that the AI processes and incorporates into its brand assessment.

SENTIMENT SOURCE 5 – Competitive Comparisons & Third-Party Analysis

Every time a review site, a consultant, an industry analyst, or a comparison article positions your brand against competitors, it creates a comparative sentiment signal. Being consistently described as ‘the premium option’, ‘the reliable choice’. Or ‘the industry standard’ builds positive sentiment through repetition. Being described as ‘overpriced,’ ‘hard to deal with,’ or ‘losing ground to competitors’ in the same contexts creates the opposite effect.

LLMs aggregate these comparative mentions to build a relative sentiment position, not just ‘is this brand positive or negative’ but ‘how does this brand compare to its alternatives – and that relative positioning directly influences which company gets recommended when a user is choosing between options.

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The Bottom Line – Your Unknown Scores are Already Being Set

Every day your business operates online, the Big 5 LLMs are forming and refining their assessment of your brand. Your Authority Score is being shaped by whether your expertise is visible, deep, and independently confirmed. Your Sentiment Score is being shaped by every review written, every news story published, every social mention made, and every forum thread stated about your company.

The businesses that will dominate Ai-generated recommendations in their industries are not necessarily the most technically advanced or the most established. They are the ones that understand how these evaluations work and build consistent, deliberate programs to feed the right signals. That work starts with knowing where you stand – and it starts right now!

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