> ## Documentation Index
> Fetch the complete documentation index at: https://docs.finseo.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Sentiment

> Analyze how AI describes your brand, competitors, criticisms, and awards.

# Sentiment

Sentiment shows how AI systems describe brands in your tracked prompt results. It goes beyond visibility and answers a different question:

```text theme={"system"}
When AI mentions us or our competitors, is the framing positive, neutral, or negative?
```

## What Sentiment measures

Finseo analyzes competitor and brand mentions in AI answers and extracts:

* Sentiment score
* Positive, neutral, and negative phrase counts
* Aspect-level scores
* Criticisms and risks
* Awards and "best for" claims
* Reputation trend over time
* Comparison win rate where comparative claims are available

## Brands tab

The **Brands** tab is a leaderboard of brands mentioned in your tracked prompts.

It shows:

* Mentions
* Average sentiment score
* Positive/neutral/negative distribution
* Strongest aspects
* Weakest aspects
* Your own brand marker
* Reputation trend where available

Use it to compare your brand against competitors in the same prompt set.

## Scorecard tab

The **Scorecard** tab compares brands across aspects such as:

* Quality
* Price
* Value
* Performance
* Features
* Reliability
* Design
* Support
* Service
* Ease of use
* Availability

Green cells mean AI framed the brand positively for that aspect. Red cells mean the framing was negative. Neutral cells are factual or mixed.

## Criticism tab

The **Criticism** tab surfaces negative phrases found in AI answers.

Each row can include:

* Brand
* Aspect
* Negative phrase
* Score
* Model
* Source domain where available
* Prompt/result context

Use this to find reputation risks and repeated objections, for example:

```text theme={"system"}
"expensive for smaller teams"
"limited integrations"
"poor availability"
"not ideal for enterprise use"
```

## Awards tab

The **Awards** tab shows "best for" style claims and recommendation labels extracted from AI answers.

Examples:

* Best for agencies
* Best budget option
* Best for enterprise
* Best for ecommerce
* Best for beginners

This helps you see which buying situations AI associates with each brand.

## Filters

Sentiment supports the same analysis filters as other tracking views:

* Date range
* AI model
* Tags

Use tags to compare prompt groups such as:

* `branded`
* `unbranded`
* `comparison`
* `product-category`
* `de`
* `us`

## Where the data comes from

Sentiment is generated from tracked prompt results. When a prompt runs, Finseo analyzes the AI answer, extracts brand mentions, and stores sentiment phrases and aspect scores.

If a prompt result has no extracted sentiment phrases, it may still count as a mention, but it will not contribute as much to aspect-level sentiment views.

## How to improve sentiment data quality

* Track enough prompts in each market and category.
* Include comparison and recommendation prompts, not only brand prompts.
* Use tags to separate prompt groups.
* Enable the models your audience actually uses.
* Review the Criticism tab regularly and update content to address repeated objections.
* Use Awards to see where your positioning is already strong.

## Visibility vs sentiment

Visibility and sentiment are different:

| Metric     | Question                                                 |
| ---------- | -------------------------------------------------------- |
| Visibility | Does AI mention the brand?                               |
| Position   | Where does the brand appear compared with competitors?   |
| Sentiment  | How positively or negatively does AI describe the brand? |
| Awards     | What use cases or "best for" labels does AI assign?      |
| Criticism  | What negative claims or objections appear repeatedly?    |

A brand can be highly visible but framed negatively. It can also be mentioned less often but with stronger sentiment. Use both views together.