Sentiment Analysis

Overview

Every time an AI model responds to one of your prompts, AEO Optima automatically scores the sentiment of that response. Sentiment analysis tells you not just whether AI models mention your brand, but how they talk about it — positively, neutrally, or negatively.

A brand that appears in 80% of AI responses but is described negatively has a very different challenge than a brand that appears in 40% of responses but is described positively. Sentiment analysis gives you this critical second dimension of visibility intelligence.

How Sentiment Scoring Works

When a snapshot is captured, the AI response text is analyzed for tone, word choice, and contextual framing related to your brand and topic. Each response receives one of three sentiment classifications:

• Positive — The response uses favorable language, recommends your brand, highlights strengths, or positions you as a leading option.
• Neutral — The response mentions your brand factually without strong positive or negative framing. Informational references and balanced comparisons typically fall here.
• Negative — The response uses unfavorable language, highlights weaknesses, warns users about limitations, or positions competitors as clearly superior.

Sentiment is assessed per snapshot, meaning the same prompt can receive different sentiment scores from different AI models or at different points in time.

Three Views of Sentiment

The Sentiment page provides three distinct views to help you understand the data from different angles.

Overview

A donut chart showing the overall distribution of positive, neutral, and negative sentiment across all snapshots in the selected date range.

This view answers the question: "Overall, how is AI talking about my brand?"

A healthy distribution typically shows a majority of positive and neutral sentiment. A large negative segment warrants investigation.

By LLM Provider

A breakdown of sentiment per AI model. This view shows you which models are most favorable and which are least favorable toward your brand.

This view answers the question: "Which AI engine has the most positive (or negative) perception of my brand?"

It is common to see significant variation between models. For example, one model may describe your brand very positively while another is more neutral or critical. These differences often reflect variations in training data, recency of information, and model-specific behavior.

By Prompt Type

A breakdown of sentiment per prompt category or type. This view reveals how sentiment varies depending on the kind of question being asked.

This view answers the question: "What types of questions lead to the most positive or negative responses about my brand?"

Common patterns include:

• Brand awareness prompts tend to produce neutral or positive responses.
• Comparison prompts often produce more mixed or negative sentiment, because AI models attempt to present a balanced view that includes competitor strengths.
• Problem/solution prompts vary widely based on whether AI models associate your brand with the solution.

Taking Action on Sentiment Data

Understanding sentiment is only valuable if it drives action. The following table provides a framework for interpreting common sentiment patterns and responding to them.

Reading Sentiment Trends

Sentiment is not static. AI models are regularly retrained and updated, which means sentiment can shift over time. Key things to watch for:

Gradual Shifts

A slow drift from positive toward neutral may indicate that competitors are strengthening their own AI presence, causing AI models to present a more balanced view. This is a signal to reinvest in content and visibility efforts.

Sudden Changes

A sharp change in sentiment — especially toward negative — may indicate:

• A widely reported negative event or PR issue affecting your brand.
• A change in an AI model's training data or behavior.
• New competitor content that repositions the landscape.

When you spot a sudden change, use the snapshot detail view to read the actual AI responses and understand what triggered the shift.

Model-Specific Trends

Track sentiment per model over time. If one model's sentiment is improving while another's is declining, it helps you understand which information sources each model is drawing from and where to focus your efforts.

Tip: Pair sentiment analysis with your Competitor Intelligence data. If your sentiment declines while a competitor's Share of Voice increases, the two trends may be connected — competitors may be producing content that AI models prefer.

Sentiment and Other Features