The problem
Sentiment on broadcast coverage is often estimated from a listen-through or a headline, which is subjective and hard to defend. Without the actual words, tone analysis is a guess, and a client asking 'why is this rated negative?' has no evidence to point to.
How Whipscribe helps
Whipscribe transcribes broadcast and podcast coverage into accurate, timestamped text so sentiment is analysed on the verbatim words — feeding your own or a third-party sentiment model, with every rating traceable back to the segment and the exact phrasing.
Sentiment on the real words
Analyse tone from verbatim transcripts instead of a listen-through or a headline.
Traceable ratings
Each sentiment call ties back to the segment and phrasing, so it's defensible to the client.
Feed any model
Export clean text into your own or a third-party sentiment/NLP pipeline.
Private & scalable
Self-hosted Whisper, pay-as-you-go — process a campaign's coverage without sending audio to a third-party transcription service.
Questions
Do you score sentiment for us?
Whipscribe provides the accurate transcript that sentiment analysis runs on; you apply your own or a third-party sentiment tool to that clean text.
Why not analyse the audio directly?
Sentiment models work on text — an accurate transcript makes the analysis more precise and auditable than working from audio alone.
Is client coverage private?
Yes — self-hosted Whisper, nothing sent to a third-party AI service, no training on your uploads, retention you control.
Note: Whipscribe is a transcription tool that makes recordings searchable; it complements your recording and retention systems, it does not replace them, and nothing here is legal advice. Confirm your current obligations with your licence conditions and the applicable code.