Feedback: audio-intelligence-sentiment-analysis

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/audio-intelligence/sentiment-analysis
Category: audio-intelligence
Generated: 05/08/2025, 4:32:53 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:32:52 pm

Technical Documentation Analysis: Sentiment Analysis

Overall Assessment

This documentation provides comprehensive code examples and API coverage, but has several areas for improvement in structure, clarity, and user guidance.

Specific Actionable Feedback

1. Missing Critical Information

API Rate Limits & Quotas

• Add section on rate limits, concurrent request limits, and usage quotas
• Include pricing implications for sentiment analysis feature

Audio Requirements & Limitations

## Audio Requirements
- **Supported formats**: MP3, WAV, M4A, FLAC, etc.
- **Maximum file size**: [specify limit]
- **Maximum duration**: [specify limit]
- **Minimum audio quality**: [specify requirements]
- **Language detection**: Automatic vs manual language specification

Error Handling Examples

• Add comprehensive error scenarios with specific error codes
• Include retry logic examples
• Document common failure cases and troubleshooting

2. Structural Improvements

Reorganize Content Hierarchy

# Sentiment Analysis

## Overview
[Brief description with use cases]

## Quick Start
[Simplest possible example]

## Configuration Options
[All parameters explained]

## Advanced Usage
[Speaker labels, combining with other features]

## API Reference
[Complete API documentation]

## Troubleshooting
[Common issues and solutions]

Add Navigation Elements

• Table of contents for long sections
• “What’s next” suggestions
• Related feature cross-references

3. Clarity Issues to Fix

Ambiguous Confidence Score Explanation Current: “confidence score for each result” Better:

### Understanding Confidence Scores
- **Range**: 0.0 to 1.0
- **Interpretation**:
  - 0.8-1.0: Very confident
  - 0.6-0.8: Moderately confident
  - 0.0-0.6: Low confidence
- **Recommendation**: Consider human review for scores below 0.7

Unclear Timestamp Units

• Explicitly state timestamps are in milliseconds throughout
• Add timestamp conversion examples

4. Enhanced Examples Needed

Real-World Use Case Examples

## Use Cases

### Customer Service Analysis
```python
# Analyze support call sentiment over time
for result in transcript.sentiment_analysis:
    if result.confidence > 0.8 and result.sentiment == "NEGATIVE":
        print(f"Potential issue at {result.start/1000}s: {result.text}")

Meeting Sentiment Tracking

[Example showing sentiment changes over meeting duration]

**Better Sample Output**
- Show complete realistic output, not truncated
- Include multiple sentiment types in examples
- Add interpretation commentary

### 5. User Pain Points to Address

**API Key Management**
```markdown
## Authentication Setup
### Environment Variables (Recommended)
```bash
export ASSEMBLYAI_API_KEY="your_api_key_here"

Security Best Practices

• Never commit API keys to version control
• Use environment variables or secure key management
• Rotate keys regularly

**Async Processing Clarification**
```markdown
## Understanding Processing Flow
1. **Upload**: Audio file uploaded to AssemblyAI
2. **Queue**: Transcription job queued (immediate response with job ID)
3. **Processing**: Sentiment analysis runs (time varies by audio length)
4. **Polling**: Client checks status until completion
5. **Results**: Full sentiment analysis available

### Estimated Processing Times
- 1 minute audio: ~30-60 seconds
- 10 minute audio: ~2-5 minutes
- 60 minute audio: ~10-20 minutes

6. Technical Improvements

Add Webhook Example

## Using Webhooks (Recommended for Production)
Instead of polling, receive automatic notifications:

```python
config = aai.TranscriptionConfig(
    sentiment_analysis=True,
    webhook_url="https://your-app.com/webhook",
    webhook_auth_header_name="Authorization",
    webhook_auth_header_value="Bearer your_token"
)

**Batch Processing Example**
```python
# Process multiple files efficiently
files = ["file1.mp3", "file2.mp3", "file3.mp3"]
jobs = []

for file in files:
    transcript = aai.Transcriber().transcribe(file, config)
    jobs.append(transcript.id)

# Check all jobs
for job_id in jobs:
    transcript = aai.Transcript.get_by_id(job_id)
    if transcript.status == "completed":
        # Process sentiment results

7. Missing Integration Guidance

Combining Features

## Feature Combinations

### Sentiment + Entity Detection
```python
config = aai.TranscriptionConfig(
    sentiment_analysis=True,
    entity_detection=True,
    speaker_labels=True
)

# Correlate entities with sentiment
for sentiment in transcript.sentiment_analysis:
    related_entities = [e for e in transcript.entities
                       if e.start <= sentiment.end and e.end >= sentiment.start]

### 8. FAQ Improvements

**Add More Practical FAQs**
```markdown
### How accurate is sentiment analysis for different accents?
### Can I analyze sentiment for specific speakers only?
### How does background noise affect sentiment accuracy?
### What languages work best with sentiment analysis?
### How do I handle mixed languages in one audio file?

9. Quick Wins

Add Success Response Example Show the complete JSON response structure immediately after the request example.

Include cURL Examples Add cURL examples alongside SDK examples for each major section.

Add “Try It” Section

## Try It Now
Test sentiment analysis with our sample audio file:
```bash
curl -X POST https://api.assemblyai.com/v2/transcript \
  -H "Authorization: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"audio_url": "https://assembly.ai/wildfires.mp3", "sentiment_analysis": true}'

This feedback addresses the most critical gaps in user experience, technical completeness, and documentation clarity while maintaining the existing comprehensive code coverage.

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