How accurate is AI-on-AI sentiment analysis?

The platform uses a dedicated sentiment model that is separate from the AI engines being monitored. Accuracy is validated against a human-labelled dataset of AI-generated brand mentions and currently achieves over 90% agreement with human raters on overall polarity and over 80% on aspect-level classification. Edge cases — such as sarcasm or highly technical qualifications — are flagged for manual review when confidence is low.

Can I see exactly which sentences drove a negative sentiment score?

Yes. Every sentiment classification links back to the specific sentences in the AI response that influenced the score. You can click through from a negative trend on the dashboard to the individual mention, see the highlighted sentences, and understand exactly what the engine said that drove the rating down.

Feature

diagnosis

Sentiment Analysis

Understand the tone AI engines use when they talk about your brand and track sentiment shifts over time.

Quick answer

Being mentioned by an AI engine is not always a good thing. If ChatGPT recommends your product but follows it with caveats about pricing, reliability, or support, the mention may hurt more than it helps. Sentiment Analysis evaluates the tone and framing of every AI-generated mention of your brand so you can distinguish positive recommendations from neutral descriptions and negative warnings.

How it works

Sentiment Analysis in detail

Every mention captured by Brand Mention Tracking is passed through a multi-dimensional sentiment pipeline. The pipeline uses a combination of fine-tuned language models and rule-based heuristics to classify overall sentiment (positive, neutral, negative, mixed) and extract aspect-level sentiment for dimensions such as product quality, pricing, support, reliability, and ease of use.

Aspect-level classification lets you see that an engine is positive about your product features but negative about your documentation, for example. Each aspect score is stored with the mention record and rolled up into aggregate charts on the sentiment dashboard.

The historical view shows sentiment trend lines per engine, per competitor, and per aspect dimension. Annotations mark key events — model updates, content changes you made, competitor launches — so you can correlate sentiment shifts with real-world causes. Smart Alerts can be configured to fire when overall sentiment drops below a threshold or when a specific aspect dimension flips from positive to negative.

Benefits

Why Sentiment Analysis matters

Distinguish positive recommendations from mentions with caveats or criticism

Identify which aspects of your brand AI engines view positively and which they question

Compare your sentiment against competitors to find narrative gaps

Track sentiment trends to measure the impact of content changes

Trigger alerts when sentiment drops on any engine or dimension

Use cases

When to use Sentiment Analysis

A product team discovers that AI engines consistently describe the brand as "powerful but complex" and creates simplified onboarding content.

A marketing director compares brand sentiment across ChatGPT, Perplexity, and Gemini to identify which engine needs the most attention.

A content strategist tracks sentiment improvement after publishing a series of customer success stories.

A competitive analyst surfaces that a rival's sentiment dropped after a public outage, creating a window of opportunity.

FAQ

Sentiment Analysis FAQ

Related features

Brand Mention Tracking

detection

Know exactly when and where AI engines mention your brand, across every model and every prompt that matters.

Smart Alerts

detection

Get notified the moment your AI visibility changes — before small shifts become big problems.

Hallucination Detection

diagnosis

Automatically detect when AI engines make incorrect claims about your brand and trigger corrective action.

Content Optimization Scoring

resolution

Score every page on five dimensions of citation readiness and know exactly what to fix.

View all features

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Start with the pages and proof that AI can actually use

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