Sentiment Analysis

What is Sentiment Analysis?

Sentiment analysis turns unstructured text into a label a workflow can act on. In go-to-market the input is usually a reply, a call transcript or a support message, and the output decides what happens next: a positive reply routes to a rep and pauses the sequence, an out-of-office reschedules, a referral redirects to a new contact, an opt-out suppresses the record permanently. Older rule-based classifiers were poor at this because the signal often sits in phrasing rather than vocabulary — 'not right now, try me next quarter' contains no negative words but is not a yes, and 'unsubscribe me' and 'please stop emailing' need identical handling despite sharing nothing lexically. Language models handle that far better. Two cautions remain: sentiment and intent are not the same thing, since a polite decline reads positive and converts at zero, and any classification that triggers suppression should be tuned to over-suppress rather than risk contacting someone who asked you not to.

Why it matters

• Makes reply handling automatable at volumes no team could read manually.
• Modern models catch phrasing-level meaning that keyword rules systematically miss.
• Sentiment is not intent — a warm decline reads positive and converts at nothing.

Use cases

• Reply routing. Replies sorted into interested, not now, referral, negative and opt-out, each handled differently.
• Opt-out detection. Any request to stop recognised and suppressed regardless of how it was worded.
• Call analysis. Transcripts scored for objection patterns and sentiment shifts across the conversation.