Sentiment Score Analysis

This article explains how sentiment analysis works in Limy and how to identify and address negative or inconsistent AI framing of your brand.

Overview

Sentiment Analysis shows how AI systems describe and frame your brand within AI-generated answers.

When AI platforms generate responses about your brand, they do more than reference facts. They apply tone and context - positive, neutral, or negative - based on the sources they ingest and the narratives they infer. Over time, this framing shapes how users perceive your brand before they ever interact with your website or content directly.

Sentiment Analysis in Limy allows you to understand how AI systems talk about your brand, identify where negative or inconsistent framing exists, and take targeted action to improve brand perception across AI-generated answers.

You can access Sentiment Analysis from the Sentiment page in the left navigation. Sentiment insights are also available directly within the Dashboard and at the individual prompt level, allowing you to analyze brand perception across high-level trends and specific AI-generated answers.

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What Sentiment Analysis Shows

Sentiment Analysis allows you to:

• See an overall sentiment score for your brand

• Track sentiment trends over time

• Analyze sentiment at the prompt level

• Compare sentiment across competitors

• Identify prompts and sources driving negative or positive framing

Sentiment is measured on a 0–100 scale, where:

• 0 represents strongly negative framing

• 100 represents strongly positive framing

This score reflects AI-generated language, not customer reviews or social sentiment.

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How to Access Sentiment Analysis

You can access Sentiment Analysis in multiple places within Limy:

1. Directly within the Dashboard

1. From the Sentiment page in the left navigation

1. At the individual prompt level

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How to Use Sentiment Analysis

Step 1: Review overall sentiment

Start by reviewing your overall sentiment score and trend line to understand how AI framing of your brand changes over time.

This view helps identify:

• Gradual improvements or declines

• Sudden shifts caused by new sources or narratives

Step 2: Analyze sentiment at the prompt level

Click into individual prompts to see how sentiment is expressed within specific AI-generated answers.

Prompt-level analysis helps you:

• Identify which topics drive negative framing

• Understand the context behind sentiment changes

• Avoid treating sentiment as a single, abstract metric

Step 3: Identify influencing sources

For prompts with negative or mixed sentiment, review the underlying sources AI systems rely on.

Negative framing often originates from:

• Blog posts

• Reviews

• Forum or community discussions (e.g., Reddit)

• Outdated or misleading content

Understanding these sources allows you to take targeted corrective action.

Sentiment improvement is typically incremental and driven by addressing specific narratives, not publishing content at scale.

How Sentiment Analysis Connects to Other Limy Features

Sentiment Analysis works closely with:

• Prompts: to identify where sentiment is formed

• Sources & Citations: to understand what drives framing

• Recommendations: to address sentiment issues through targeted actions

• Attribution: to evaluate whether improved sentiment correlates with outcomes

Together, these features help manage brand perception in AI search, not just visibility.

Use Sentiment Analysis to identify prompts and sources where AI systems frame your brand negatively. Review the underlying content (such as blogs or forum posts) and take targeted action - like updating content or adding clarifying, positive commentary - to influence how AI systems interpret your brand over time.

FAQs

Is sentiment based on customer reviews or social media?

No. Sentiment is derived from AI-generated answers and the language used by AI systems, based on the sources they reference.

Can sentiment change quickly?

Yes. Sentiment can shift when AI systems ingest new sources or re-interpret existing information.

How can I improve negative sentiment?

Improvement usually involves addressing the underlying sources through updated content, PR efforts, or clarifying commentary.

Should I optimize for sentiment or visibility first?

Both matter, but addressing negative sentiment on high-visibility prompts should be prioritized.

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Relevant Content

Sentiment Analysis: Prompt LevelPrompts OverviewRecommendationsSource Analysis

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