Sentiment Analysis

nonBot AI analyzes the sentiment of brand mentions across AI responses. Understanding sentiment helps you gauge brand perception and identify reputation risks or opportunities.

Sentiment Categories

Positive sentiment appears when AI responses portray your brand favorably through recommendations, endorsements, highlighting strengths, favorable comparisons to competitors, or praise for features and value. For example: "Brand X is known for excellent customer support and competitive pricing."

Neutral sentiment indicates factual mentions without opinion—listing your brand alongside others, describing what your product does, or providing objective information without evaluative language. For example: "Brand X offers project management software with task tracking features."

Negative sentiment reflects unfavorable mentions including pointing out weaknesses, warnings or cautions, unfavorable comparisons, or mentions of problems and complaints. For example: "Brand X has been criticized for its steep learning curve."

Mixed sentiment contains both positive and negative elements such as balanced reviews mentioning pros and cons, conditional recommendations, or "it depends" responses. For example: "Brand X offers powerful features but may be too complex for beginners."

Viewing Sentiment Data

The dominant sentiment KPI card shows your most common sentiment type for a quick overview of overall perception. The detailed view provides a full breakdown with percentages across all four categories. In the Results tab, each individual response shows its sentiment classification so you can see exactly how AI described your brand, identify specific statements, and understand the context.

Interpreting Sentiment

For most brands, the ideal sentiment profile shows majority positive or neutral mentions with minimal negative sentiment. Some mixed sentiment is normal and can actually appear more authentic than universally positive responses.

Watch for warning signs: increasing negative sentiment over time, consistent negative sentiment across multiple AI providers, or negative sentiment concentrated in specific topics. Positive signs include growing positive sentiment, positive mentions in competitive comparisons, and recommendations in key use cases.

Responding to Sentiment

If you see negative sentiment, review the specific AI responses in the Results tab and identify the concerns being raised. Determine whether the criticism is valid, then take action by updating website content to address concerns, improving product areas cited, or creating content to counter misinformation.

For positive sentiment, identify which strengths are being highlighted and reinforce these messages in your content. Ensure claims are accurate and up-to-date, and build on areas where you're perceived positively.

Mixed sentiment often indicates your product serves some use cases well but not others, there's a learning curve or trade-off, or you have recognized strengths alongside known limitations. Respond by emphasizing ideal use cases in your content, addressing limitations proactively, and providing resources for common challenges.

Sentiment Trends

Track sentiment changes over time. Improving sentiment may indicate successful optimization while declining sentiment may signal emerging issues. Some industries experience seasonal patterns that affect sentiment.

Limitations

Sentiment analysis has inherent limitations. AI providers phrase things differently, context affects interpretation, sarcasm or nuance may be misclassified, and small sample sizes affect reliability. Use sentiment as one input among many rather than the sole measure of brand health.

Next Steps

Compare your sentiment to competitors in Competitor Detection, generate content to improve perception with Content Ideas, or review industry recommendations in Best Practices.