Text & Content Analysis

Context: entity (food & health see)  |  Window: 24h Β· 7d Β· 30d  |  Corpus: 1 articles

This page analyzes how a selected category, entity, or narrative frame appears in recent news coverage within the specified time window. Metrics and breakdowns are derived from the active article corpus.

Corpus KPIs

Entity
food & health see
Window
Last 7 days
Articles
1
From
2026-08-14 11:00:30+00
To
2026-08-14 11:00:30+00
Range
2026-08-07 19:27:35.405324+00 β†’ 2026-08-14 19:27:35.405324+00
Who’s being talked about

Top Entities

Entity Label Mentions Actions
baltimore GPE 1 πŸ“Š πŸ” 🧲
cnn ORG 1 πŸ“Š πŸ” 🧲
courtesy cnn underscored WORK_OF_ART 1 πŸ“Š πŸ” 🧲
ellen mcalpine PERSON 1 πŸ“Š πŸ” 🧲
facebook tweet email link threads link copied WORK_OF_ART 1 πŸ“Š πŸ” 🧲
food health see ORG 1 πŸ“Š πŸ” 🧲
george washington university ORG 1 πŸ“Š πŸ” 🧲
geriatrics ORG 1 πŸ“Š πŸ” 🧲
leana wen PERSON 1 πŸ“Š πŸ” 🧲
neurology PERSON 1 πŸ“Š πŸ” 🧲
Who’s talking

Top Sources (Domains)

Domain Articles % Actions
cnn.com 1 100.00 πŸ“Š πŸ“° 🧲
How the stories are being framed

Top Narrative Frames

Frame Weight Actions
Technology 0.7800 πŸ“Š 🧲
Business, Companies 0.1200 πŸ“Š 🧲
Health 0.0500 πŸ“Š 🧲
Political Theatre 0.0300 πŸ“Š 🧲
Food and Beverage 0.0100 πŸ“Š 🧲
How it feels

Sentiment

Label Articles Actions
😐 Neutral 1 πŸ“Š 🧲

Articles included

Top entities
Top sources
Top narrative frames
Pub Date Category Domain Title Analyze Author Sent Score
2026-08-14 11:00:30+00 Health cnn.com What β€˜super movers’ can teach us about healthier brains Analyze Katia Hetter 😐 neutral 0.15