AI-102 Practice Question: Implement natural language processing solutions
A development team is using the Azure Cognitive Service for Language to perform sentiment analysis on social media posts. They notice that the returned sentiment scores are often neutral for posts that are clearly positive or negative. What is the most likely reason?
⚠ Common exam trap
Candidates often assume the service is failing due to language or configuration issues, when in fact the neutral default is a deliberate design choice to avoid false positives on very short, ambiguous input.
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
✓
The posts are too short, causing the sentiment detection to default to neutral.
The Azure Cognitive Service for Language sentiment analysis API has a minimum text length requirement for reliable scoring. When input text is very short (e.g., a few words or a single sentence), the model lacks sufficient context to confidently assign a positive or negative score, so it defaults to a neutral score (often around 0.5). This is a documented behavior of the API, not a limitation of social media language support.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The service does not support sentiment analysis for social media language.
Why it's wrong here
The service supports social media language.
- ✓
The posts are too short, causing the sentiment detection to default to neutral.
Why this is correct
Short texts provide insufficient context for accurate sentiment detection.
- ✗
The service is not configured to detect mixed sentiment.
Why it's wrong here
Mixed sentiment is supported, but the issue is likely short text.
- ✗
The posts are in a language that is not supported by the sentiment analysis API.
Why it's wrong here
The service supports many languages; this is not the likely cause.
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