A team wants to find the main topics across product reviews. Which technique fits?
Why C is the answer
Keyword extraction
Keyword extraction pulls out the main terms from text.
In detail
- Keyword extraction pulls out the main terms from text.
- Pair it with sentiment to see what people feel about each topic.
Use case
An illustrative scenario, not a real incident.
A product team scans thousands of reviews for main topics. Keyword extraction surfaces "battery life" and "delivery".
See it in different ways
flowchart LR A["Text"] --> B["Keyword extraction: main terms"] A --> C["Entity recognition: people, places"] A --> D["Sentiment: positive or negative"] A --> E["Summarization: shorter version"]
mindmap
root((AI workloads))
Text
Sentiment
Key phrases
Entities
Speech
Recognition
Synthesis
Vision
Classification
Object detection
OCR
Documents
Information extraction
Generative
Text
Imagesflowchart LR S0["Reviews"] --> S1["Keyword extraction"] --> S2["Top terms"]
Key point: Main terms come from keyword extraction.
Why the other options are wrong
- A. Image generation from a text prompt
- Image generation creates images.
- B. Text to speech
- Text to speech creates audio.
- D. Object detection
- Object detection works on images.