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Understanding Sentiment in Scrunch

A guide to understanding how Scrunch Sentiment helps brands track how AI models describe them.

Updated this week

What Sentiment Measures

Scrunch includes a Sentiment Score to help you understand how your brand is being described in AI-generated responses. This is a qualitative measure of brand perception:

  • Positive: Language is favorable or endorsing

  • Mixed: Includes both pros/cons or neutral observations

  • Negative: Highlights drawbacks or positions your brand as less desirable than another option

This metric is powered by a machine learning (ML) model trained to evaluate tone and positioning across thousands of AI outputs.


How Negative Sentiment Is Triggered

Unlike traditional social listening tools, Scrunch’s sentiment model isn’t just looking for negative keywords. Instead, it captures relative sentiment:

  • The most common case: your brand is mentioned alongside competitors, and the AI recommends a different option.

    • Example: “For your use case, definitely go with [Competitor],” while your brand is mentioned but not recommended.

  • It is rare for an AI to outright say “Brand X is bad”, since most are tuned to maintain a neutral or optimistic tone.

This means that a “Negative” flag usually signals competitive disadvantage in positioning, not explicit criticism.


Current Limitations

  • At present, the dashboard does not provide a one-click list of “Here are the 21 negative mentions.”

  • You can manually explore negative sentiment by filtering Prompts or Citations and cross-referencing the sentiment column in your dashboard.

  • For more granular tracking, you can use the API or Looker Studio connector to slice sentiment data down to the prompt or citation level.


What’s Coming Next

We’re actively working on:

  • More granular filters across Prompts and Citations (e.g. “show only negative sentiment prompts”).

  • Drill-down views in charts to tie sentiment directly to the exact prompts and sources behind it.

  • Expanded Insights that will highlight sentiment-related opportunities, including where competitor mentions are pushing you down in AI results.


How to Use Sentiment Data Today

  1. Identify weak spots: Check where sentiment is marked negative and note if a competitor is favored. Leverage the 'Brand Protection' category in the Insights tab for help identifying these prompts and responses.

  2. Prioritize fixes: Focus on the prompts/citations driving negative sentiment.

  3. Take action:

    • Strengthen content around those topics.

    • Consider PR or backlink strategies to shift how third-party sources present your brand.

    • Track improvements as new data is collected.


Key Takeaway

Scrunch’s sentiment analysis helps you understand not just if you’re being mentioned, but how you’re being positioned. This metric provides early signals of competitive disadvantage so you can act before it impacts perception more broadly.

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