AI-102 Practice Question: Implement knowledge mining and information extraction solutions
You are using Azure AI Language to perform entity recognition on customer feedback. You need to identify the sentiment expressed towards specific entities. Which feature should you use?
⚠ Common exam trap
It's easy for candidates to confuse Named Entity Recognition (NER) with the ability to extract sentiment about entities, but NER only identifies entities without any sentiment analysis, while opinion mining is the specific feature that combines entity detection with sentiment scoring.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Sentiment analysis with opinion mining
Sentiment analysis with opinion mining is the correct feature because it not only detects the overall sentiment of a text but also associates specific sentiments with particular entities or aspects mentioned in the text. This allows you to determine, for example, that a customer feels positively about 'product quality' but negatively about 'customer support', which is exactly what the question requires.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Named Entity Recognition (NER)
Why it's wrong here
NER detects and classifies entity mentions but returns no opinion or polarity attached to them, so sentiment towards each entity stays unknown. It is tempting because it isolates the entities themselves, which is the correct choice when you only need to extract people, places and organisations from text.
- ✓
Sentiment analysis with opinion mining
Why this is correct
Opinion mining extends sentiment analysis by associating each detected entity with its own sentiment and the specific words expressing it, rather than returning one document-level score. This directly satisfies the requirement to identify sentiment towards specific entities in the feedback.
- ✗
Entity linking
Why it's wrong here
Entity linking disambiguates a detected mention to a knowledge-base entry such as a Wikipedia article; it produces no sentiment score. It is tempting because it resolves which specific real-world entity is referenced, which is the correct choice when identity disambiguation, not opinion, is the requirement.
- ✗
Key phrase extraction
Why it's wrong here
Key phrase extraction returns salient noun phrases only; it assigns no sentiment polarity to the entities you must evaluate. It is tempting because it summarises what feedback discusses, which is the correct choice when you need topic discovery or word-cloud style themes rather than per-entity opinion.
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