Text Analysis: Keywords, Entities & Sentiment
Text analysis lets AI understand the meaning behind words. Learn the four key techniques the exam tests: keyword extraction, entity detection, sentiment analysis, and summarisation.
What is text analysis?
Text analysis is when AI reads text and tells you what it’s about, how the writer feels, and what the key facts are.
Imagine you have 10,000 customer reviews. You can’t read them all. Text analysis AI reads every single one and tells you: “85% are positive, people love the delivery speed, the main complaint is about packaging, and the most mentioned product is the wireless earbuds.”
It doesn’t create new content — it understands existing text.
The four key techniques
1. Keyword extraction
What it does: Identifies the most important terms and phrases in a document.
Example input: “Microsoft announced a new AI platform called Foundry that combines model deployment, agent creation, and enterprise governance into a single Azure service.”
Keywords extracted: Microsoft, AI platform, Foundry, model deployment, agent creation, enterprise governance, Azure
DataFlow Corp scenario: DataFlow processes 50,000 support tickets per month. Keyword extraction automatically tags each ticket with relevant topics (billing, outage, performance) for routing and reporting.
2. Entity detection (Named Entity Recognition)
What it does: Identifies and classifies specific entities in text — people, places, organisations, dates, amounts.
| Entity Type | Example | Category |
|---|---|---|
| Person | ”Dr. Sarah Chen” | Person |
| Organisation | ”MediSpark” | Organisation |
| Location | ”Auckland, New Zealand” | Location |
| Date | ”April 21, 2026” | DateTime |
| Money | ”$4.5 million” | Quantity |
| ”support@medispark.com” | Contact |
GreenLeaf scenario: GreenLeaf scans hundreds of supplier contracts. Entity detection automatically extracts supplier names, contract dates, payment amounts, and delivery locations.
3. Sentiment analysis
What it does: Determines whether text expresses positive, negative, neutral, or mixed feelings.
| Text | Sentiment | Confidence |
|---|---|---|
| ”The product is amazing, best purchase ever!” | Positive | 98% |
| “Delivery was slow and the box was damaged” | Negative | 91% |
| “The order arrived on Tuesday” | Neutral | 85% |
| “Love the quality but hate the price” | Mixed | 76% |
MediSpark scenario: MediSpark analyses patient feedback surveys. Sentiment analysis flags negative responses about wait times for immediate review, while tracking overall satisfaction trends.
Opinion mining — beyond simple sentiment
Opinion mining goes deeper than sentiment analysis by identifying what specific aspect the sentiment is about:
- “The food was amazing” → food: positive
- “The service was terrible” → service: negative
- “The location is convenient but the parking is awful” → location: positive, parking: negative
This is called aspect-based sentiment analysis and is available in Azure AI Language.
4. Summarisation
What it does: Condenses long text into a shorter version while preserving key information.
Two types:
- Extractive summarisation — pulls the most important sentences directly from the source
- Abstractive summarisation — generates new sentences that capture the meaning (uses generative AI)
| Feature | Extractive | Abstractive |
|---|---|---|
| How it works | Selects key sentences from the original text | Generates new sentences that summarise the meaning |
| Accuracy | Words are exactly from the source | May rephrase — risk of minor inaccuracies |
| Natural flow | Can feel choppy (sentences from different parts) | Reads more naturally, like a human summary |
| Technology | Traditional NLP models | Generative AI (LLMs) |
| Best for | Legal documents, contracts (exact wording matters) | Meeting notes, news articles, reports |
Where text analysis fits in Azure
| Service | Capability | Type |
|---|---|---|
| Azure AI Language (Foundry Tools) | Keyword extraction, entity detection, sentiment analysis, summarisation | Dedicated NLP service |
| GPT-4o / multimodal models | All text analysis tasks + generation | General-purpose LLM |
Exam tip: Azure AI Language is a dedicated service optimised for text analysis. But GPT-4o can also do text analysis as part of a broader conversation. Know that both options exist.
🎬 Video walkthrough
Flashcards
Knowledge Check
DataFlow Corp wants to automatically categorise 50,000 monthly support tickets by topic. Each ticket should be tagged with relevant topics like 'billing', 'outage', or 'account access'. Which text analysis technique is most appropriate?
MediSpark analyses patient feedback: 'Dr. Chen was incredibly kind, but the 3-hour wait was unacceptable. The facility at 42 Queen Street was clean.' Which text analysis technique identifies 'Dr. Chen', '42 Queen Street', and '3-hour' as specific entities?
Next up: Speech: Recognition & Synthesis — how AI converts spoken words to text and text to natural-sounding speech.