Social Listening and Sentiment Monitoring
Monitoring what is actually said about you, scored at the level of the specific thing people are reacting to, with alerting that distinguishes an ordinary busy day from something that needs a response tonight.
Most social listening dashboards show a sentiment line that nobody acts on, because a single company-wide sentiment score compresses away everything useful. Whether people are unhappy about delivery, pricing or a specific feature are three different problems with three different owners.
Social listening monitors public mentions of a brand, product, competitor or topic across social platforms, forums, review sites and news, classifying what is being discussed, the sentiment toward each aspect, and whether a change warrants action.
Why generic sentiment tools disappoint
| Problem | What goes wrong | What we do |
|---|---|---|
| One score per mention | Mixed opinions averaged into neutral | Aspect-level scoring per topic |
| Domain language | Industry terms scored wrongly | Calibration on your own mentions |
| Sarcasm and irony | Confidently scored backwards | Measured, flagged, reported as a known limit |
| Volume without weight | A viral post equals a quiet complaint | Weighting by reach and author influence |
| Brand name ambiguity | Unrelated mentions counted as yours | Disambiguation tuned to your name |
| No route to action | A dashboard nobody opens | Routing to a named owner per aspect |
Calibration against your own reviewers is what turns a sentiment number into a measurement. We publish the agreement rate between the system and your team alongside the results, because a sentiment figure without one is a number with no unit.
What we build
Aspect extraction from your own mentions
The topics people actually discuss — delivery, pricing, a specific feature, support, a named competitor comparison — derived from your data rather than imposed from a generic taxonomy, so each aspect can have an owner who can act on it.
Alerting that distinguishes noise from a crisis
Volume spikes are common and mostly harmless. What matters is an unusual rate of change in negative sentiment on a specific aspect, weighted by reach, against a seasonally aware baseline. Alerting on raw volume produces a fortnight of false alarms and then gets muted.
Competitor and category monitoring
The same aspects tracked for named competitors, which turns your own trend into something interpretable — a category-wide dip in sentiment about delivery is a different problem from one that is yours alone.
Routing to someone who can act
Product complaints to product, service failures to support, factual errors to communications, safety or legal matters escalated immediately. Monitoring that ends in a dashboard is a subscription; monitoring that ends in a ticket is a capability.
Honest handling of what cannot be read
Sarcasm, in-jokes, dialect and context-dependent meaning are measured for accuracy rather than assumed. We report the error rate for these categories rather than presenting a uniform confidence across all mentions.
Data access is a licensing question before it is a technical one
Platform terms govern what may be collected, stored and analysed, and they differ by platform and change. We work through official APIs and licensed data providers, and where a source's terms do not permit collection we say so rather than working around it. Public mentions can also contain personal data, which brings retention and lawful-basis obligations with them.
What it is good for, and what it is not
- Early warning on a product or service problem, often before it reaches support volume.
- Evidence for product decisions, since unprompted complaints are a less biased signal than survey responses.
- Competitive intelligence on how the category talks about alternatives.
- Crisis detection with a defined escalation path, which is the use case that justifies the alerting work.
- Not a representative sample of your customers. People who post publicly are a self-selecting minority, and treating social sentiment as customer sentiment is a well-worn mistake.
That last point is worth stating in the reporting itself rather than only at kickoff, because the temptation to read a social sentiment line as customer satisfaction is strong and the two are not the same thing.
How the engagement runs
The system is calibrated against your own reviewers before any number is reported.
Sources and licensing
Platforms, forums, review sites and news sources assessed for coverage and permitted use.
Aspects and calibration
Aspects derived from your real mentions; your team scores a sample and the system is tuned to agree.
Build
Collection, disambiguation, aspect-level scoring, reach weighting and competitor tracking implemented.
Alerting and routing
Thresholds set against a seasonal baseline; routing to named owners with an escalation path.
Handover
Reporting cadence, calibration refresh and the stated limitations documented.
What you receive
Aspect-level monitoring calibrated against your own team, routed to people who can act.
Source and licensing assessment
Coverage per source and what its terms permit, with excluded sources named.
Aspect model
Topics derived from your own mentions, each with a named owner.
Calibrated sentiment scoring
Tuned to your domain, with the agreement rate against your reviewers published.
Alerting
Rate-of-change thresholds on negative sentiment by aspect, weighted by reach, on a seasonal baseline.
Competitor tracking
The same aspects for named competitors, so your trend is interpretable.
Routing and escalation
Tickets to owners, with an immediate path for safety, legal and crisis matters.
Is this the right engagement?
Worth being direct. Social Listening and Sentiment Monitoring is the wrong spend in some situations, and those are listed rather than buried.
Good fit if
- Mention volume is beyond what a team can read.
- A previous issue was noticed late because nobody was watching.
- Product decisions would benefit from unprompted feedback at scale.
- An existing tool produces a sentiment line nobody acts on.
- Competitor positioning in the category needs tracking.
Choose something else if
- Mention volume is low enough to read manually, which is better.
- The requirement is what AI assistants say about you. See AI visibility monitoring.
- The sources you need cannot be accessed under their terms.
- No owner will act on the alerts, which makes it a dashboard subscription.
Frequently asked questions
Marked up with FAQPage schema so these answers can surface directly in search results and inside AI assistant responses.
How accurate is sentiment analysis?
Good on clear positive and negative statements, weaker on sarcasm, irony and domain-specific language — and the honest figure comes from calibrating against your own reviewers on your own mentions. We publish that agreement rate alongside results, because a sentiment number without one is a number with no unit.
Why aspect-level rather than one score?
Because a mention praising the product and criticising delivery is not neutral, it is two separate signals with two different owners. A single company sentiment score compresses away everything actionable, which is why so many social listening dashboards go unopened after the first month.
How do you avoid alert fatigue?
By alerting on unusual rate of change in negative sentiment for a specific aspect, weighted by reach, against a seasonally aware baseline — not on raw volume. Volume spikes are common and mostly harmless, and a system that alerts on them gets muted within a fortnight and then misses the real one.
Is social sentiment the same as customer sentiment?
No, and conflating them is a common and costly mistake. People who post publicly are a self-selecting minority, skewed toward the very satisfied and the very unhappy. Social listening is excellent early warning and competitive intelligence; it is not a representative measure of your customer base, and we state that in the reporting itself.
How is this different from AI visibility monitoring?
This tracks what people say about you in public. AI visibility monitoring tracks what AI assistants say about you when a buyer asks — which is increasingly the first thing they encounter, and which no amount of social monitoring will show you.
Often paired with this
Most clients combine two or three engagements from the AI for Marketing and Growth pillar. These are the ones that most often run immediately before or after.
AI Visibility and Brand Monitoring in LLMs
Tracked prompts across surfaces, share of mention against competitors, and correction of inaccurate claims.
Read more →Marketing Analytics and Attribution AI
Incrementality experiments and mix modelling, with honest treatment of what attribution cannot resolve.
Read more →Conversion Rate Optimisation with AI
AI to find what to test and read behaviour at scale; proper experiment discipline to decide what worked.
Read more →Is this the right engagement?
Tell us what you are trying to build. If a different service fits better, or if you do not need us at all, we will say so.