Social sentiment and theme analysis
A sentiment score on its own is a mood ring. It tells you the room turned negative and nothing whatsoever about what turned it. Sence reads each comment in context, then clusters what people are actually arguing about into named themes, so the number arrives with its reason attached.
ThemesRedmount Rovers26,558 comments
Match-day atmosphere5,312 (20%)
Controversial selections of players3,187 (12%)
Refereeing frustration2,656 (10%)
@playmaker_12
Ka rawe tēnei tīma, ka kitea koutou ā te Hātarei!
Positive
This team is awesome, see you all on Saturday!Translated
Trusted by teams using Sence
How the number gets its reason
Themes26,558 comments
Match-day atmosphere5,312 (20%)
Controversial selections of players3,187 (12%)
Refereeing frustration2,656 (10%)
Read in context, not scored on keywords
Sence analyses entire conversations rather than matching words against a sentiment lexicon. Sarcasm, slang, frustration and excitement are read for what they mean in that thread, which is precisely where word-level scoring gets it backwards.
Cluster into named themes
Comments that are about the same thing are grouped and given a name a human would use: the delivery times, the pricing change, the new kit, the display spec. Each theme carries its own sentiment split, so you can see which one is dragging the total down.
Quantify, tag and track
Themes, brand mentions, emerging topics and any custom tag you define are quantified and tracked dynamically over time. A theme that was a handful of comments last month and a recurring complaint this month shows up as a trend rather than as an anecdote somebody screenshotted.
Beyond social listening's sentiment score
@playmaker_12
Ka rawe tēnei tīma, ka kitea koutou ā te Hātarei!
Positive
This team is awesome, see you all on Saturday!Translated
Sence uses brand-calibrated sentiment rather than a generic score, so a community that talks bluntly is not filed as a community that hates you, and criticism that genuinely matters does not disappear into the noise of its own tone.
Dynamic thematics and brand-calibrated sentiment reveal what content drives engagement, what sparks backlash and where customer intent is emerging, which makes the next content decision an evidence question instead of an argument.
Alongside the themes Sence detects, you can fully customise and automate tags against your own brand criteria, and every comment is classified against them. Your reporting then uses your team's vocabulary rather than a vendor's.
Sence detects and tracks trending topics before they become mainstream and measures shifts in community interest over time, so a theme is something you lead rather than something you answer for.
Conversation spikes and sentiment shifts are measured as they happen. A sharp fall is the earliest public sign that something has gone wrong, and it moves in your comments before it moves anywhere you would think to look.
Insights are synthesised and quantified rather than handed over as raw comments, which removes the hours a team spends compiling a monthly read by hand.
What gets classified, and how
Every comment on a connected channel is classified on arrival, in over 45 languages, with no manual tagging. Sence analyses entire conversations rather than scoring individual words, and it runs no Boolean filters. Sence's own published comparison puts classic social listening at around 70% accuracy against roughly 97% or better for conversational intelligence. That is a comparison of insight quality, not a moderation benchmark, and it is worth knowing which one a vendor is quoting you.
- Comments on Facebook, Instagram, YouTube and TikTok
- Sentiment classified in over 45 languages, with no manual tagging
- Sarcasm, slang, frustration and excitement read in context
- Themes, brand mentions, emerging topics and custom tags, all quantified
- Historical data retained, so a theme can be tracked for years
- No Boolean filters, and no scraping of the open web
Works with
The pass that produces these themes is the same pass that hides a comment breaching your rules, which is why comment and DM moderation and this page are describing one model doing two jobs.
Understand
The pillar this sits in: one classification pass, four different questions answered off it.
Learn moreAudience Intelligence
Who is behind the sentiment: the segments, the superfans and the voices other people follow.
Learn moreBrand & Competitive Intelligence
The same themes, read for the brands inside them and benchmarked against your competitors.
Learn moreFrequently asked questions
Sence's published comparison puts classic social listening at around 70% accuracy and conversational intelligence at roughly 97% or better. The gap is context: Sence analyses entire conversations rather than scoring individual words, so it separates sarcasm from praise and reads slang the way the person meant it. That figure describes insight quality and is not a moderation benchmark.
Yes, and this is where a generic model fails hardest. A community that talks bluntly is not a community that has turned on you. Sentiment is brand-calibrated and the classifier is built from your own decisions, so your audience's normal tone reads as normal and a real shift reads as a shift.
No. Themes are detected from the conversation itself, so a topic nobody thought to search for still surfaces. On top of that you can define your own tags, and every comment is classified against them automatically.
It replaces the compiling, not the thinking. Insights are synthesised and quantified in the dashboard, which removes the hours spent pulling a report together by hand. What to do about a theme that is growing is still your team's call.
Both live in the same place. The comments behind a theme can be hidden, tagged or replied to from the same workflow that analysed them, because moderation and insight run on one classification pass rather than in two tools.