Text & Content Analysis
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.
Explore by Category
Corpus KPIs
Entity
late last year
Articles
2
From
2026-04-30 22:17:22+00
To
2026-05-04 10:30:00+00
Range
2026-04-29 07:01:00.062005+00 →
2026-05-06 07:01:00.062005+00
Who’s being talked about
Top Entities
| Entity | Label | Mentions | Actions |
|---|---|---|---|
| late last year | DATE | 2 | 📊 🔍 🧲 |
| one | CARDINAL | 2 | 📊 🔍 🧲 |
| a bienz | PERSON | 1 | 📊 🔍 🧲 |
| 2026 tags subsaharan | EVENT | 1 | 📊 🔍 🧲 |
| 9 million | MONEY | 1 | 📊 🔍 🧲 |
| a adam | PERSON | 1 | 📊 🔍 🧲 |
| aaron tays | PERSON | 1 | 📊 🔍 🧲 |
| acl | ORG | 1 | 📊 🔍 🧲 |
| advocates | WORK_OF_ART | 1 | 📊 🔍 🧲 |
| african | NORP | 1 | 📊 🔍 🧲 |
| ai | ORG | 1 | 📊 🔍 🧲 |
| alexa | ORG | 1 | 📊 🔍 🧲 |
| american meteorological society | ORG | 1 | 📊 🔍 🧲 |
| apha | ORG | 1 | 📊 🔍 🧲 |
| askhistorians | NORP | 1 | 📊 🔍 🧲 |
| at least 300 | CARDINAL | 1 | 📊 🔍 🧲 |
| at least 65 | CARDINAL | 1 | 📊 🔍 🧲 |
| ben williamson | PERSON | 1 | 📊 🔍 🧲 |
| binis | PERSON | 1 | 📊 🔍 🧲 |
| buffalo | GPE | 1 | 📊 🔍 🧲 |
| bulletin | NORP | 1 | 📊 🔍 🧲 |
| chicago | GPE | 1 | 📊 🔍 🧲 |
| chris ayers photographylicensed | PERSON | 1 | 📊 🔍 🧲 |
| citations news | ORG | 1 | 📊 🔍 🧲 |
| classroom resources for this article power words readability score | ORG | 1 | 📊 🔍 🧲 |
Articles included
Top entities
Top sources
Top narrative frames
| Pub Date | Category | Domain | Title | Analyze | Author | Sent | Score |
|---|---|---|---|---|---|---|---|
| 2026-05-04 10:30:00+00 | Science |
|
Don’t trust AI for help with citations, science-fair judges warn | Analyze | Kathryn Hulick | 😐 neutral | 0.0439 |
| 2026-04-30 22:17:22+00 | Health |
|
Health Policy Watch: Ghana rebuffs US health deal – but South Africa and Zambia struggle without aid | Analyze | EATG Communication | 😐 neutral | -0.0004 |