Customer Experience
Catch the angry ticket before it becomes a churned account.
Customer Support Sentiment Analyzer is customer sentiment analysis software for teams that want the reasoning shown, not hidden. Plans start at $49 a month with a 14-day free trial.
Support teams usually find out a customer was unhappy when the cancellation email arrives. The warning signs were in the ticket thread weeks earlier — they were just spread across hundreds of conversations nobody had time to read. Sentiment Pulse scores every conversation, groups them by what people are actually complaining about, and shows exactly which words produced each score.
Most sentiment tools return a number between -1 and 1 and expect you to trust it. When that number disagrees with your reading of the ticket, there is nothing to inspect and no way to correct it. Agents stop believing the tool, and a dashboard nobody believes is a dashboard nobody opens.
Every score here traces back to the specific terms, modifiers and signals that produced it. Open a conversation and you see which phrases pushed it negative, which intensifiers amplified them, and what the final weighting was. If the engine is wrong, you can see why, and you can fix it by editing the lexicon rather than filing a support request.
Counting positive and negative words gets you a tool that fails on ordinary English. Three failure modes matter in support text specifically, and each is handled explicitly.
A falling average tells you something is wrong but not what. Conversations are clustered by what they are about — billing, onboarding, performance, the interface, support responsiveness — so a drop can be traced to a cause.
Each topic carries its own volume and sentiment trend. A topic that is small but sharply negative and growing is usually the one worth acting on first, and it is the one a single platform-wide average hides completely.
The pipeline is deterministic. The same text always produces the same score, which means a number in a report can be reproduced months later — something a hosted language model cannot promise you.
Because it is a lexicon rather than a model, it is also editable. Industry terms that read as negative in general English but are neutral in yours — "charge", "dispute", "claim" in financial services, say — can be reweighted once and apply everywhere from then on.
Two or three agents and no analyst. The value is triage: urgent and angry tickets surface first instead of being handled in arrival order.
Account-level sentiment trends across every conversation, so a quiet account drifting negative is visible before renewal rather than after.
Topic clustering turns support volume into a ranked list of what frustrates people, with the verbatim quotes attached for the ones worth reading.
Deterministic scoring that can be reproduced and audited, with no customer text sent to a third-party model.
Score every support conversation for sentiment, emotion and urgency, cluster the topics driving complaints, and raise escalation alerts when an account's tone trends negative — with a clear audit of why each message scored the way it did.
Customer Support Sentiment Analyzer starts at $49 a month with a 14-day free trial. Thematic is about $2,000 per month per company.
Yes, and that is the main reason to pick this. Every score lists the exact terms, their base weight, the applied weight after negation or intensifiers, and the signals detected. No score appears without its reasoning.
Both. Negators flip polarity on the words they govern, intensifiers scale it, and in a sentence like "X was great, but Y was broken" the clause after the contrast carries the verdict — because that is where the real message is.
On Growth and Scale you can add custom terms with your own weights. "Stockout" is neutral English but always bad for your customers, and the model should know that.
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