Cross-Surface Sentiment Audit
Why review sites, Reddit, and app stores tell different stories about your brand — and which leads.
The Cross-Surface Sentiment Audit measures sentiment on your brand across four surface types — review sites, Reddit, Google Reviews, and app store reviews — and reports a variance map of where surfaces agree, where they diverge, and which surface tends to lead the others by quarter.
The deliverable is a sentiment report with a leading-versus-lagging analysis, a crisis-window or pre-launch deep-dive option, and the underlying scored corpus as a companion data file. Base scope covers four surfaces over a twelve-month window; quarterly tracking is +$499 per quarter ongoing.
Built for challenger-brand CMOs, agencies, and PR-and-comms leaders who need to know which signal to act on first.
Why does Trustpilot say one thing and Reddit say another — and which leads?
What buyers actually ask.
Why does Trustpilot say one thing about my brand and Reddit say another — and which leads?
The Cross-Surface Sentiment Audit answers exactly this. Each surface is scored independently, divergences are named, and the leading-versus-lagging analysis surfaces which surface tends to move first. Reddit often leads curated review sites by a quarter or more; the audit names the lag for your brand specifically.
Which surfaces does the audit cover?
Trustpilot, Reddit, Google Reviews, and App Store reviews at the base scope. Hacker News and Twitter/X are available as additional surfaces during scope. We adapt the surface selection to where conversation about your brand actually happens.
How is sentiment scored?
Per-surface sentiment models calibrated against the linguistic conventions of each surface — Reddit's sarcasm density is materially different from Trustpilot's pattern. Per-surface calibration matters; a single sentiment model across all four would over-call positive on Reddit and under-call positive on Trustpilot.
What is "leading versus lagging"?
Some surfaces move first when brand sentiment shifts; others lag. For most consumer brands, Reddit leads Trustpilot by one to two quarters. The audit measures the lead-lag pattern empirically for your brand and names the leading surface so monitoring effort is concentrated.
Can I run this around a launch or crisis window?
Yes. The base scope is twelve months; for launch and crisis windows we tighten to a focused window with daily-resolution time series. Scope this at intake.
How does quarterly tracking work?
+$499 per quarter ongoing. Each quarter re-runs the four-surface scoring, recomputes divergence and lead-lag, and reports the QoQ delta. The deliverable is a delta report plus an updated companion data file.
How is this different from a brand-monitoring tool?
Monitoring tools deliver a stream the analyst has to interpret. The audit delivers the interpretation — variance map, leading surface, named drivers, deep-dive on the divergences worth attention. Buyers commonly run both.
The deliverable, in detail.
- Sentiment scoring across four surface types — Trustpilot, Reddit, Google Reviews, App Store reviews — at the base scope.
- Variance map naming where surfaces agree on themes, where they diverge, and the direction of divergence.
- Leading-versus-lagging surface identification with cross-correlation analysis on time-series sentiment per surface.
- Crisis-window or pre-launch deep-dive option, scoped at intake with tighter time windows and higher temporal resolution.
How the report is built.
The Cross-Surface Sentiment Audit pulls the named-brand corpus across four surfaces at the base scope: Trustpilot, Reddit, Google Reviews, and App Store reviews. Each surface's scrape window covers the previous twelve months at the base scope; tighter windows are used for crisis-and-launch deep dives.
Sentiment is scored with per-surface models calibrated against the linguistic conventions of each surface. Reddit sarcasm and Trustpilot performative-positive patterns are surface-specific signals that a single cross-surface model would mishandle. Theme clustering runs in parallel — what people are saying, not just whether it is positive.
Cross-surface variance is computed at the theme level: where surfaces agree on a theme they appear in the agreement layer; where they diverge they appear in the variance map with the divergence direction named. The leading-versus-lagging analysis runs cross-correlation on time-series sentiment per surface to identify the surface that tends to move first for your brand.
A senior analyst reviews the joined dataset before the report is drafted. The Counter-Signal Pass surfaces the strongest opposing case for any divergence finding — review-bombing campaigns, organic ratings inflation, demographic-skew effects on the surface population.
Quarterly tracking, when added, re-runs the methodology at quarter-end and reports QoQ deltas on every layer.
Counter-Signal Pass is included on every report. The full Foragentis methodology is documented in The State of AEO and GEO in 2026.
What this report does NOT do.
Procurement-grade reports scope themselves. The work below is adjacent and important — and is not in this SKU.
The Cross-Surface Sentiment Audit reads public surfaces. Internal NPS, customer-support transcripts, and proprietary panel data are out of scope. The audit can be triangulated against internal data but does not undertake to substitute for it.
Sentiment scoring is a model-driven step. Per-surface calibration is the strongest safeguard, but models do not match human raters on every theme. The audit names the themes where model-rater agreement is strong versus weak so the buyer reads the result accordingly.
Cross-surface lead-lag is a statistical pattern, not a causal claim. Reddit may lead Trustpilot for a brand because of demographic skew, posting cadence, or moderation policy rather than because Reddit is the leading indicator of the underlying brand sentiment. The audit reports the empirical lead-lag and the alternative explanations.
What the engagement costs.
The Counter-Signal Pass — every thesis stress-tested against its strongest opposing case — is included on every report at no extra cost. See the Counter-Signal block on the catalog hub →
Methodology preview on request.
A redacted public sample for this SKU is in production. To preview the methodology now, email forintel@foragentis.com and we will send the methodology one-pager. The published methodology white paper — The State of AEO and GEO in 2026 — covers the underlying analytical framework.
Adjacent reports.
About Foragentis.
Foragentis is an AI research and product company based in Sacramento, California. ForIntel is the business-intelligence research arm — producing custom dossiers across four buyer lanes: Search & AI Visibility, Markets & Locations, Capital & Innovation, and Specialty.
Every claim in a ForIntel report traces to a public source. Findings are re-verified before delivery. The Adversary/Analyst architecture pairs a senior analyst with a counter-signal pass on every thesis. Anything below our statistical thresholds is labeled directional rather than validated.
Methodology is documented in The State of AEO and GEO in 2026 — a 9,900-word, 42-page public study with effect-size statistics across four frontier AI engines.
Ready to commission the report?
Intake takes under five minutes. We confirm scope, timeline, and cost within one business day.