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

# Sentiment

> Monitor how AI responses describe your brand and fix negative narrative trends early

<Frame>
  <img src="https://mintcdn.com/atomicai/2EwXzkqhhawPmJOp/images/data/ai-search/app-ai-search-sentiment-all.png?fit=max&auto=format&n=2EwXzkqhhawPmJOp&q=85&s=cdb378ee8b29bca997e8c4f608bbcfb9" alt="AI Search Sentiment page with trend, positive and negative split, and All tab" width="1536" height="1024" data-path="images/data/ai-search/app-ai-search-sentiment-all.png" />
</Frame>

Use this page to track brand perception quality in AI answers.

<div className="atomic-info-callout">
  <p>
    <strong>Important:</strong> One negative mention is noise. Repeated negative
    themes across prompts are the real risk.
  </p>
</div>

## Questions this page should answer

1. Is sentiment improving or getting riskier?
2. Which themes are driving positive or negative perception?
3. What messaging updates should we ship first?

## Before you analyze

* Match date range with the rest of AI Search reporting.
* Read trend direction before row-level details.
* Separate positive and negative themes before acting.

## What this page gives you

* Positive sentiment trend line.
* Positive vs negative share summary.
* Theme-level evidence table with sources.
* Three subtabs: `All`, `Positive`, and `Negative`.

## How to read the top sentiment section

* Trend line shows narrative direction over time.
* Right-side split shows current narrative balance.
* Theme rows show what is shaping that balance.

<div className="atomic-highlight-card">
  <p>
    <strong>Key signal:</strong> If visibility stays stable but negative share
    rises, conversion risk is usually increasing before traffic metrics react.
  </p>
</div>

## How these metrics are calculated (simple)

### Positive share

```text theme={null}
Positive share = (Positive-labeled responses / total sentiment-labeled responses) x 100
```

### Negative share

```text theme={null}
Negative share = (Negative-labeled responses / total sentiment-labeled responses) x 100
```

### Theme rows

Theme rows are recurring sentiment statements grouped into actionable theme clusters.

## All tab

Start in `All` for a full narrative baseline.

<Frame>
  <img src="https://mintcdn.com/atomicai/2EwXzkqhhawPmJOp/images/data/ai-search/app-ai-search-sentiment-all.png?fit=max&auto=format&n=2EwXzkqhhawPmJOp&q=85&s=cdb378ee8b29bca997e8c4f608bbcfb9" alt="AI Search Sentiment with All tab selected" width="1536" height="1024" data-path="images/data/ai-search/app-ai-search-sentiment-all.png" />
</Frame>

Use it to identify:

* Mixed themes requiring clearer positioning.
* Repeated concerns that hurt trust.
* Positive messages worth scaling.

## Positive tab

Use `Positive` to preserve and scale what already works.

<Frame>
  <img src="https://mintcdn.com/atomicai/2EwXzkqhhawPmJOp/images/data/ai-search/app-ai-search-sentiment-positive.png?fit=max&auto=format&n=2EwXzkqhhawPmJOp&q=85&s=9b938a7fe24753a881f616a81786ea95" alt="AI Search Sentiment with Positive tab selected" width="1536" height="1024" data-path="images/data/ai-search/app-ai-search-sentiment-positive.png" />
</Frame>

Focus on:

* Positive themes that repeat across sources.
* Signals you can reuse in landing pages and prompts.
* Source types that produce high-trust mentions.

## Negative tab

Use `Negative` to reduce risk quickly.

<Frame>
  <img src="https://mintcdn.com/atomicai/2EwXzkqhhawPmJOp/images/data/ai-search/app-ai-search-sentiment-negative.png?fit=max&auto=format&n=2EwXzkqhhawPmJOp&q=85&s=a03687dfdb2d322d23627fd7c846f403" alt="AI Search Sentiment with Negative tab selected" width="1536" height="1024" data-path="images/data/ai-search/app-ai-search-sentiment-negative.png" />
</Frame>

Focus on:

* Recurring negative themes.
* Negative themes tied to decision queries.
* Source patterns that repeatedly create risk.

## Quick weekly checklist

1. Check positive vs negative balance.
2. Flag top recurring negative theme.
3. Protect one high-impact positive theme.
4. Assign one message/proof fix per sprint.

## How to use filters

* Start with `All` before narrowing.
* Compare `Positive` and `Negative` separately.
* Keep window consistent to avoid false trend shifts.

## What to fix first

| Pattern in Sentiment data              | What it usually means              | Recommended action                            |
| -------------------------------------- | ---------------------------------- | --------------------------------------------- |
| Negative share rising on trust topics  | Credibility concern                | Add stronger proof and clearer claims         |
| Positive stable, conversions weakening | Narrative not supporting decisions | Improve decision-stage messaging              |
| One negative theme repeats             | Structural positioning gap         | Update core narrative and FAQ/support content |
| Positive themes come from few sources  | Fragile narrative base             | Expand high-quality source distribution       |

## Team routine

1. Weekly: review drift and assign fixes.
2. Bi-weekly: verify whether negative themes were reduced.
3. Monthly: align messaging roadmap with sentiment trends.

## Keep in mind

* Sentiment is directional, not absolute truth.
* Short windows can be noisy.
* Repeated themes matter more than isolated mentions.

## Where to go next

* [Citations](/data/ai-search/citations)
* [Prompts](/data/ai-search/prompts)
* [Pages](/data/ai-search/pages)
* [Overview](/data/ai-search/overview)