Feedback: guides-call-sentiment-analysis

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/call-sentiment-analysis
Category: guides
Generated: 05/08/2025, 4:42:30 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:42:29 pm

I’ll analyze this documentation and provide specific, actionable feedback to improve its clarity, completeness, and user experience.

Critical Missing Information

1. Prerequisites and Setup

• Missing: Clear installation instructions for the AssemblyAI Python SDK
• Add:

  pip install assemblyai

• Missing: Python version requirements
• Missing: How to actually obtain and locate the API key in the dashboard

2. Error Handling

• Critical Gap: No error handling examples or common error scenarios
• Add: Error handling for failed transcriptions, invalid API keys, unsupported file formats, and LeMUR API failures

3. Expected Output

• Major Issue: The documentation ends with “The output will look similar to the example below” but provides no example output
• Add: Complete sample output showing actual sentiment analysis results

Unclear Explanations

1. LeMUR Concept

• Problem: LeMUR is introduced with acronym explanation but lacks clear value proposition
• Improve: Add a brief explanation of why LeMUR is better than traditional sentiment analysis

2. Context Parameters

• Problem: The purpose and impact of agent_context and customer_context are unclear
• Improve: Explain how context affects analysis accuracy and provide guidelines for writing effective context

3. Model Selection

• Problem: claude3_5_sonnet model is used without explanation of alternatives or selection criteria
• Add: Brief explanation of available models and when to use each

Structural Improvements

1. Reorganize Content Flow

# Analyze Customer Call Sentiment with LeMUR

## Overview
[Brief explanation of what this guide accomplishes]

## Prerequisites
- AssemblyAI account with API key
- Python 3.7+
- Installation instructions

## Quick Start
[Current quickstart code]

## Understanding the Output
[Sample output with explanations]

## Detailed Walkthrough
[Step-by-step with better explanations]

## Customization Options
[How to modify for different use cases]

## Error Handling
[Common issues and solutions]

## Next Steps
[Related features and advanced usage]

2. Code Organization

• Problem: Step-by-step section repeats quickstart without adding value
• Solution: Make step-by-step section truly educational with explanations between each step

Better Examples Needed

1. Audio File Examples

• Add: Information about supported audio formats
• Add: File size limitations
• Add: URL example alongside local file example

2. Real-World Context Examples

# Different business scenarios
contexts = {
    "support_call": "Customer calling about a technical issue with their product",
    "sales_call": "Sales representative trying to close a deal with a potential customer",
    "retention_call": "Customer service trying to prevent customer from canceling service"
}

3. Question Variations

• Add: Examples for different industries (healthcare, finance, retail)
• Add: Examples for different analysis goals (compliance, training, satisfaction)

User Pain Points to Address

1. Pricing Transparency

• Problem: Mentions LeMUR pricing tiers but doesn’t explain costs
• Solution: Add approximate cost information or link to detailed pricing

2. File Processing Time

• Missing: No mention of processing time expectations
• Add: Information about typical processing times for different file sizes

3. API Limits

• Missing: No mention of rate limits or file size restrictions
• Add: Clear documentation of limitations

Specific Code Improvements

1. Add Error Handling

try:
    transcript = transcriber.transcribe("./meeting.mp3")
    if transcript.status == aai.TranscriptStatus.error:
        print(f"Transcription failed: {transcript.error}")
        return
except Exception as e:
    print(f"Error during transcription: {e}")
    return

2. Add Input Validation

import os

audio_file = "./meeting.mp3"
if not os.path.exists(audio_file):
    raise FileNotFoundError(f"Audio file not found: {audio_file}")

3. Improve Output Formatting

for i, qa in enumerate(result.response, 1):
    print(f"\n--- Question {i} ---")
    print(f"Q: {qa.question}")
    print(f"A: {qa.answer}")
    print("-" * 50)

Additional Sections to Add

1. Troubleshooting Section

• Common transcription issues
• LeMUR API errors
• Audio quality problems

2. Best Practices

• Optimal audio quality recommendations
• Writing effective context and questions
• When to use different LeMUR models

3. Integration Examples

• How to integrate with existing call center systems
• Batch processing multiple files
• Storing and analyzing results over time

This documentation has good foundational content but needs significant improvements in completeness, error handling, and user guidance to be truly production-ready.

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