AI sentiment analysis measures the tone of AI-generated answers that reference your brand—classifying mentions as positive, neutral, or negative, then aggregating those signals across prompts, platforms, and time. In the era of answer engines, sentiment complements AI brand mentions, share‑of‑voice, and citation metrics to provide a qualitative view of reputation.
Why sentiment in AI answers matters
- Perception driver: Tone shapes trust and preference beyond mere inclusion.
- Risk detection: Negative framings often point to outdated info or unmet expectations.
- Narrative control: Persistent neutrality in evaluative prompts signals missed opportunities to highlight strengths.
- Content feedback: Shifts in tone reveal whether messaging and docs resonate.
What to measure
- Mention‑level sentiment: The tone of each brand reference within a response.
- Response‑level sentiment: Overall tone of the answer when multiple brands appear.
- Positioning language: Descriptors tied to capabilities and use cases (e.g., “best for enterprise”).
- Co‑mentions: Tone when competitors are present in the same answer.
- Platform split: Differences across ChatGPT/Claude (training‑led) vs Perplexity/Google AI (retrieval‑led).
Methodology considerations
- Unit of analysis: Score at both response and brand‑mention levels.
- Normalization: Account for platform style and verbosity before comparisons.
- Ambiguity handling: Use neutral when tone is non‑directional.
- Human‑in‑the‑loop: Review edge cases (sarcasm, caveated praise) to calibrate.
- Sampling: Use a stable prompt set to avoid confounding changes.
LLM Pulse implementation
- Automatic scoring: Positive/neutral/negative classification per captured response.
- Breakdowns: Sentiment by platform, prompt tag, competitor set, and time window.
- Trend analysis: Detect sentiment drift after content/PR events and launches.
- Drill‑downs: Read exact answers and citations behind shifts.
Optimization playbook
- Diagnose negatives: Identify recurring claims or missing proof behind skeptical tone.
- Fix accuracy: Refresh features, pricing, integrations; add update notes with dates.
- Add evidence: Publish case studies, benchmarks, and customer quotes.
- Clarify comparisons: Provide transparent X vs Y tables and “best‑for” guidance.
- Seed broadly: Repurpose improved content to third‑party hubs and forums.
KPIs and heuristics
- Net sentiment: Positive minus negative share over a rolling period.
- Platform deltas: Where tone diverges most; prioritize fixes accordingly.
- Driver phrases: Which positioning phrases correlate with positive tone.