> ## Documentation Index > Fetch the complete documentation index at: https://docs.attensira.com/llms.txt > Use this file to discover all available pages before exploring further. # Sentiment > Learn how Sentiment measures whether AI recommends you enthusiastically or with hesitation. Understand scoring, what drives sentiment, and strategies to improve how AI describes your brand. **Sentiment measures how AI talks about you when it mentions your brand.** Getting mentioned isn't enough — what matters is whether AI recommends you enthusiastically or with hesitation. ## Understanding Sentiment When AI mentions your brand, it doesn't just list your name — it describes you. That description carries an opinion, and that opinion shapes how users perceive you before they ever visit your site. Here's the same brand mentioned with different sentiment: ``` Positive Sentiment: "For small agencies, Acme CRM is an excellent choice. Users consistently praise its intuitive interface and responsive support team. It's particularly well-suited for teams that prioritize ease of use." Negative Sentiment: "Acme CRM is an option to consider, though some users report frustration with customer support response times. The learning curve can be steep for non-technical teams, and pricing has increased significantly." Neutral Sentiment: "Acme CRM offers contact management, pipeline tracking, and email integration. It serves small to medium businesses in various industries." ``` Same brand, three very different impressions. The user reading the positive version is primed to convert. The user reading the negative version is primed to look elsewhere. ## Why Sentiment matters In traditional search, users read your content and form their own opinion. In AI search, **AI forms the opinion for them**. The way AI describes you directly shapes perception before users click through. **The impact on conversion:** | Sentiment | User behavior | Business impact | | ------------ | -------------------------------------------- | ---------------------------------------------- | | **Positive** | Users arrive pre-sold, ready to convert | Higher conversion rates, shorter sales cycles | | **Neutral** | Users arrive curious, need convincing | Normal conversion rates, typical sales process | | **Negative** | Users arrive skeptical or don't click at all | Lower conversions, objection-heavy sales calls | You can have high Share of Voice with low Sentiment — meaning you're mentioned often but described poorly. This is worse than not being mentioned at all, because you're actively being positioned negatively in users' minds. ## How Sentiment is calculated **Sentiment is analyzed directly from AI responses.** When you track a prompt, Attensira sends it to multiple AI platforms and analyzes the actual response text to score how positively or negatively each model describes you. ### Sentiment scoring Each response is scored on a 0-100 scale based on the language used: | Score | Label | What AI typically says | | ---------- | ------------- | -------------------------------------------------------------------- | | **80-100** | Very Positive | "Excellent," "highly recommend," "users love," "best-in-class" | | **70-79** | Positive | "Great option," "strong choice," "well-regarded," "popular" | | **50-69** | Neutral | "An option," "offers features," factual descriptions without opinion | | **30-49** | Negative | "Some concerns," "users report issues," "can be challenging" | | **0-29** | Very Negative | "Avoid," "significant problems," "better alternatives exist" | ### How it works in practice For each prompt you track, we get responses from multiple AI platforms and score each one: ``` Prompt: "What's the best CRM for marketing agencies?" Responses: ├── ChatGPT response: "Acme CRM is excellent for agencies..." → Score: 85 ├── Perplexity response: "Acme is a solid choice with good..." → Score: 78 ├── Claude response: "For marketing agencies, Acme offers..." → Score: 72 ├── Gemini response: "Acme CRM is well-regarded among..." → Score: 80 └── Grok response: "Acme has strong agency features..." → Score: 76 Average for this prompt: (85 + 78 + 72 + 80 + 76) / 5 = 78.2 ``` Your overall sentiment is the average across all prompts and all platforms. **A score below 50 indicates more negative mentions than positive.** ### Breaking down your sentiment You can view sentiment in multiple ways: * **Overall sentiment** — Average across all prompts and platforms * **Sentiment by platform** — How each AI model perceives you (ChatGPT might score you 82, while Perplexity scores you 68) * **Sentiment by prompt** — Which topics generate positive vs. negative responses * **Sentiment over time** — Track how perception changes week to week ## What drives Sentiment AI forms opinions about your brand based on what it reads across the web. Key sources: ### 1. Review platforms Your ratings on G2, Capterra, TrustRadius, and similar sites directly influence how AI describes you. A 4.8-star rating leads to "highly rated" descriptions. A 3.2-star rating leads to "mixed reviews." ``` High review scores → "Users consistently rate Acme highly..." Low review scores → "Acme receives mixed reviews, with some users noting..." ``` ### 2. Social media and forums Community sentiment on Twitter/X, Reddit, LinkedIn, and industry forums shapes AI's perception. Frequent complaints = negative sentiment. Enthusiastic users = positive sentiment. ### 3. Media coverage How publications write about you matters. Positive feature articles boost sentiment. Critical coverage or negative news stories drag it down. ### 4. Customer testimonials and case studies Published success stories and testimonials give AI positive language to draw from. Without them, AI has less positive material to reference. ### 5. Comparison content When third parties compare you to competitors, who wins? If comparison articles consistently favor competitors, AI absorbs that positioning. ## Sentiment vs. other metrics Understanding how Sentiment interacts with your other metrics: | Scenario | What it means | Priority action | | ---------------------------------------- | --------------------------------------------------- | ---------------------------------------- | | **High Share of Voice + High Sentiment** | Ideal state — you're mentioned often and positively | Maintain and expand | | **High Share of Voice + Low Sentiment** | Dangerous — you're visible but described negatively | Fix perception urgently | | **Low Share of Voice + High Sentiment** | Hidden gem — those who know you love you | Increase awareness | | **Low Share of Voice + Low Sentiment** | Significant work needed on both fronts | Address sentiment first, then visibility | | **High Position + Low Sentiment** | You rank well but with caveats | Improve how you're described | | **Low Position + High Sentiment** | Described well when mentioned, but mentioned late | Work on position factors | If you have high visibility but low sentiment, fixing sentiment should be your top priority. Being mentioned negatively is actively hurting you. ## Common Sentiment patterns ### The "but" problem AI often uses "but" to introduce concerns: ``` "Acme CRM is feature-rich, but users report a steep learning curve." "Acme offers good value, but customer support can be slow." ``` Track what comes after the "but" — these are your sentiment killers. ### The comparison trap AI often establishes sentiment through comparison: ``` Positive: "Unlike some competitors, Acme offers excellent support." Negative: "While competitors offer free tiers, Acme's pricing starts at $99." ``` How you compare to competitors directly affects your sentiment. ### The qualifier issue Watch for qualifiers that soften recommendations: ``` Strong: "I recommend Acme for agencies." Weak: "Acme could be worth considering for some agencies." Weaker: "If budget isn't a concern, Acme might work." ``` More qualifiers = lower sentiment, even if the overall tone seems positive. ## Improving your Sentiment ### Step 1: Diagnose the problem Identify which mentions are dragging down your score: 1. **Filter by sentiment** — Find your lowest-scoring mentions 2. **Identify patterns** — What concerns appear repeatedly? 3. **Trace to sources** — Where is AI getting this negative information? Common negative patterns: * Support complaints * Pricing concerns * Feature gaps vs. competitors * Reliability issues * Learning curve complaints ### Step 2: Address root causes Sometimes sentiment reflects real problems. If users consistently complain about support, improving support will eventually improve sentiment. **Real improvements that affect sentiment:** * Fix product issues that generate complaints * Improve customer support response times * Address common feature requests * Make onboarding easier * Adjust pricing if it's a consistent pain point ### Step 3: Generate positive signals Create content and experiences that give AI positive language to draw from: **Review management:** * Ask satisfied customers to leave reviews * Respond professionally to negative reviews * Keep review profiles current and complete * Aim for recent reviews (AI may weight recency) **Content creation:** * Publish customer success stories * Create case studies with specific results * Share testimonials on your site * Get featured in positive "best of" lists **PR and media:** * Pursue positive media coverage * Respond to negative coverage with corrections * Share company wins and milestones * Earn industry awards and recognition ### Step 4: Monitor changes Sentiment changes slowly — it takes time for new content to be indexed and for AI models to update their understanding. Track sentiment over weeks and months, not days. ``` Sentiment improvement timeline: ├── Week 1-2: New reviews/content published ├── Week 2-4: Content gets indexed ├── Week 4-8: AI models begin incorporating new data └── Week 8+: Sentiment scores start reflecting changes ``` ## Sentiment by platform Different AI platforms may perceive you differently based on their data sources: | Platform | Primary sentiment sources | | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------- | |

**ChatGPT** | Broad web content, tends toward consensus view | |

**Claude** | May weigh nuanced analysis, looks at full context | |

**Perplexity** | Heavily citation-based, recent sources matter most | |

**Gemini** | Google's index, review sites weighted heavily | |

**Grok** | Real-time X/Twitter sentiment, social signals | |

**Copilot** | Bing-indexed content, LinkedIn signals, enterprise sources | |

**Google AI Mode** | Google's index, review sites, Knowledge Graph data | |

**Google AI Overview** | Google's index, high-authority sources, featured snippet data | If your sentiment differs significantly across platforms, investigate which data sources each platform emphasizes. A platform showing lower sentiment may be drawing from sources with more negative content about you. ## Sentiment for different mention types Not all mentions carry equal weight for sentiment analysis: | Mention type | Sentiment impact | | -------------------------- | --------------------------------------------------- | | **Primary recommendation** | High impact — this is where sentiment matters most | | **Alternative mention** | Medium impact — "Also consider" mentions | | **Comparison mention** | Variable — depends on how comparison is framed | | **Feature mention** | Lower impact — often neutral, factual | | **Warning mention** | High negative impact — actively steering users away | Focus on improving sentiment in your primary recommendations first, as these have the highest conversion impact. ## Next steps How often you're mentioned overall Where AI gets its information about you