NEWv1.17: Audited & Actionable
Metrics & Analytics

Sentiment Analysis

Automated analysis of the tone and connotation (positive, negative, neutral) of brand mentions in AI responses.

What is Sentiment Analysis?

Sentiment Analysis is the process that produces the sentiment score: while sentiment is the resulting metric, sentiment analysis is the method used to classify how a brand is portrayed in AI responses. Modern approaches rely on LLM-based classification to label each brand mention as positive, neutral, or negative, often with a confidence level, and can score sentiment per mention or aggregated per answer. Running this analysis continuously across monitored prompts surfaces problematic perceptions early, so you can adjust your content and reputation strategy before negative framing becomes entrenched.

How Qwairy Makes This Actionable

Qwairy uses AI to perform automated sentiment analysis on every answer containing your brand. Get sentiment scores, view detailed breakdowns, and identify answers that need attention.

Frequently Asked Questions

Being mentioned frequently doesn't guarantee success if sentiment is negative. A brand mentioned in 80% of responses but negatively can lose to a competitor mentioned in 30% positively. Sentiment analysis helps you understand not just visibility, but perception and positioning.

Yes. Publish positive content, case studies, testimonials, and expert reviews. Address criticism directly with factual rebuttals. Build authority through thought leadership. Over time (3-6 months), LLMs incorporate fresher, more positive signals and sentiment improves.

LLM-based sentiment classification is reliable on clear-cut positive or negative mentions; nuanced cases (mixed sentiment, sarcasm, implicit comparisons) are harder for any automated system. GEO platforms link every sentiment score to the underlying AI answer, so you can open the source response, judge the context yourself, and flag answers that need attention.
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