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
github
Articles
2
From
2026-07-21 16:43:25+00
To
2026-07-23 05:12:47+00
Range
2026-07-16 20:08:26.465642+00 →
2026-07-23 20:08:26.465642+00
Who’s being talked about
Top Entities
| Entity | Label | Mentions | Actions |
|---|---|---|---|
| ai | ORG | 2 | 📊 🔍 🧲 |
| github | ORG | 2 | 📊 🔍 🧲 |
| ml | ORG | 2 | 📊 🔍 🧲 |
| ada | ORG | 1 | 📊 🔍 🧲 |
| ai solutions | ORG | 1 | 📊 🔍 🧲 |
| aibi | ORG | 1 | 📊 🔍 🧲 |
| aibi genie | ORG | 1 | 📊 🔍 🧲 |
| amazon sagemaker pipelines | ORG | 1 | 📊 🔍 🧲 |
| andrea perez isla | PERSON | 1 | 📊 🔍 🧲 |
| aws | ORG | 1 | 📊 🔍 🧲 |
| aws professional services | ORG | 1 | 📊 🔍 🧲 |
| aws professional services additionally | ORG | 1 | 📊 🔍 🧲 |
| bbva | ORG | 1 | 📊 🔍 🧲 |
| bbva tech | ORG | 1 | 📊 🔍 🧲 |
| bi | ORG | 1 | 📊 🔍 🧲 |
| carlos guerra | PERSON | 1 | 📊 🔍 🧲 |
| ci | ORG | 1 | 📊 🔍 🧲 |
| corporate | ORG | 1 | 📊 🔍 🧲 |
| cpu | ORG | 1 | 📊 🔍 🧲 |
| data modeling | ORG | 1 | 📊 🔍 🧲 |
| databricks industry data models get databricks lakehouse industry data models | ORG | 1 | 📊 🔍 🧲 |
| databricks lakehouse business data models | ORG | 1 | 📊 🔍 🧲 |
| databricks serverless | ORG | 1 | 📊 🔍 🧲 |
| dbml | ORG | 1 | 📊 🔍 🧲 |
| deep neural networks | ORG | 1 | 📊 🔍 🧲 |
Articles included
Top entities
Top sources
Top narrative frames
| Pub Date | Category | Domain | Title | Analyze | Author | Sent | Score |
|---|---|---|---|---|---|---|---|
| 2026-07-23 05:12:47+00 | Business |
|
MLOps transformation at BBVA: delivering business value faster | Analyze | AI Solutions | 😐 neutral | 0.1059 |
| 2026-07-21 16:43:25+00 | Business |
|
Lakehouse Business Data Models for Travel & Logistics | Analyze | @databricks | 😐 neutral | 0.1089 |