[nlp-analysis] Copilot PR Conversation NLP Analysis - February 13, 2026 #15375
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This discussion was automatically closed because it expired on 2026-02-20T10:34:10.466Z.
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📊 Executive Summary
Analysis Period: Last 24 hours (PRs merged between Feb 12-13, 2026)
Repository: github/gh-aw
Total PRs Analyzed: 50 Copilot-authored PRs
Total Text Items: 100 (50 titles + 50 descriptions)
Average Sentiment: +0.071 (slightly positive)
Key Finding: Copilot PRs maintain a neutral-to-positive tone with strong focus on technical quality improvements, particularly around safe outputs, MCP servers, and error handling.
🎯 Sentiment Analysis
Overall Sentiment Distribution
Sentiment Breakdown:
Average Polarity: +0.071 on scale of -1 (very negative) to +1 (very positive)
Sentiment by Content Type
Sentiment Progression Across PRs
Observations:
🏷️ Topic Analysis
Identified Discussion Topics
5 Major Topics Detected:
Topic 1 - MCP & Command Tools (28 items, 28%): Focus on tool integration, file handling, MCP servers, and command testing
tool,file,mcp,command,testTopic 0 - Data Structures & Testing (25 items, 25%): Updates to fields, functions, and test coverage
update,field,function,test,outputTopic 2 - Error Handling & Quality (16 items, 16%): Error management, agent behavior, code quality
set,error,work,agent,qualityTopic 3 - Feature Additions (17 items, 17%): New features, flags, validation support
add,support,flag,output,validationTopic 4 - Bug Fixes (14 items, 14%): Test fixes, pattern improvements
fix,test,use,call,pattern🔑 Keyword Trends
Most Important Technical Terms
Top 15 Keywords by TF-IDF Importance:
Top Phrase Patterns (Bigrams):
☁️ Topic Word Cloud
Visual Dominance: Terms like
footer,assignee,actor,schema,tool, andmcpappear most prominently, reflecting the technical focus areas of recent development.🔍 Key Insights
1. Technical Quality Focus
Copilot PRs emphasize code quality, testing, and validation. Terms like
test,validation,schema, anderrorappear frequently, indicating strong attention to reliability.2. Safe Outputs as Primary Feature
The "safe output" bigram appears 42 times, making it the second-most common technical phrase. This suggests significant ongoing development in this feature area.
3. MCP Server Integration Priority
"MCP server" appears 35 times, indicating active work on Model Context Protocol integration - a key architectural component.
4. Security-Conscious Development
The most negative sentiment PRs (scores -0.14 to -0.20) all relate to security improvements:
This is positive - negative language in security contexts reflects appropriate seriousness.
5. Consistent Documentation Standards
"Footer", "assignee", and "actor" rank as top keywords, reflecting consistent PR formatting and metadata tracking.
📈 PR Highlights
View Detailed PR Analysis
Most Positive PRs 😊
PR #15240 (+0.353)
"Add allowed-repos support to add-labels and close-issue safe"
Focus: Feature enhancement with clear benefits
PR #15219 (+0.344)
"Add unassign-from-user safe output handler"
Focus: New capability addition
PR #15237 (+0.340)
"Extract duplicate logic to safe output helper functions"
Focus: Code quality improvement through refactoring
Most Negative PRs (Security-Focused) 🔒
PR #15231 (-0.195)
"Reduce custom secret minimum length threshold from 8 to 6 characters"
Context: Security configuration adjustment
PR #15233 (-0.172)
"Fix secret prefix preservation vulnerability (CWE-200)"
Context: CVE remediation
PR #15232 (-0.139)
"Fix ReDoS in secret scanning regex patterns"
Context: Performance security fix
Note: Negative sentiment in security PRs is appropriate and expected - it reflects the serious nature of vulnerabilities being addressed.
📊 Conversation Patterns
Data Availability Note: This analysis is based on PR titles and body descriptions only. Comment thread data was not available for this run.
PR Characteristics:
Engagement Pattern:
💡 Recommendations
🎯 Continue Current Practices
Security-First Language: Maintain serious tone for security PRs - negative sentiment here is appropriate and signals proper prioritization
Structured PR Format: Current format (Problem → Solution → Context) produces clear, neutral-positive sentiment
Safe Outputs Development: This feature area shows high activity and positive reception - continue investment
Footer Repetition: The "coding agent minute survey" footer appears in 20+ PRs. Consider if this is adding value or creating noise.
Title Neutrality: Titles are perfectly neutral (-0.001 sentiment). Consider adding more descriptive language to help readers quickly understand PR impact.
Topic Balance: 28% of PRs focus on MCP/tools. Ensure other areas (documentation, testing, UX) also receive attention.
✨ Enhancement Opportunities
Highlight Benefits: PR bodies are more positive (+0.144) than titles. Consider including benefit statements in titles for better engagement.
Conversation Data: Future runs should capture PR comments/reviews for deeper analysis of interaction patterns.
Temporal Tracking: Store this analysis in cache memory to enable trend detection across multiple periods.
🔬 Methodology
NLP Techniques Applied:
Data Processing:
Libraries Used:
Data Sources:
📦 Artifacts
Generated Data Files:
conversations.csv- Cleaned text data with sentiment scoreskeywords.csv- Top 20 keywords with TF-IDF scorestopics.csv- 5 topic clusters with distributionspr_sentiment.csv- Per-PR sentiment aggregationssummary.json- Complete analysis summaryVisualizations:
🔗 Workflow Details
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