I've had fun building this out over the past year, but this project will be put on hold indefinitely. Thank you to all who have helped me out, shared feedback, and more. Now I'm onto building out GeoTap, a project for geography nerds. You get a location, point to it on a globe, and get scored based on how close you are. Think you know the world? Try it out for yourself.
Directional only:Take insights directionally. See our disclaimer for more details.
Words & Duration
Recent Episodes
Last 25 episodes
Ad Load & Sponsors Observed
Rolling ad metrics paired with the most frequent sponsors.
Avg ad time—
Avg ad percent—
Density
medium
Sponsors Observed
BETATop 5
No sponsors detected.
Top 5 Categories & Phrases
Last 25 Episodes
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| No episodes available. | ||||||||||
About Podcast Analytics: Dashboard
StrawberryData ingests episode audio, metadata, descriptions, and full transcripts from thousands of podcasts. Each episode is processed using consistent sentiment analysis, topic detection, narrative pattern tracking, ad density estimation, and episode-level trend analysis so metrics remain comparable across time, hosts, release cycles, and categories.
Analytics available for all episodes:
- Sentiment – Normalized scoring from –1 to 1 capturing tone, emotion, and conversational direction.
- Profanity detection – Measurement of explicit language frequency for brand safety and content suitability.
- Ad load – Percentage of episode time spent on ads with estimated duration.
- Questions – Questions per minute and total question count to understand discussion dynamics.
- Emotional moments – Detection of emotional markers throughout episodes.
- Sponsors detected – Identification of sponsors and brand mentions within episodes.
- Top topics – Categorized themes and discussion areas with frequency counts.
- Key phrases – Recurring terms and phrases with frequency analysis.
These insights are designed for teams in content strategy, communications, creator analytics, advertising, PR, competitive research, and podcast intelligence.
All metrics originate from one or a combination of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) pipelines and may occasionally reflect transcription noise; such cases are flagged and continuously refined across models and data sources.