[nlp-analysis] Copilot PR Conversation NLP Analysis - February 13, 2026

[github-actions[bot]]

📊 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:

• Positive: 37 items (37.0%) - Constructive improvements and feature additions
• Neutral: 55 items (55.0%) - Technical descriptions and factual updates
• Negative: 8 items (8.0%) - Security fixes and bug resolution

Average Polarity: +0.071 on scale of -1 (very negative) to +1 (very positive)

Sentiment by Content Type

Content Type	Avg Sentiment	Interpretation
PR Bodies	+0.144	Detailed descriptions are more positive (solution-focused)
PR Titles	-0.001	Titles are neutral (factual, concise)

Sentiment Progression Across PRs

Observations:

• Sentiment remains consistently neutral with occasional positive spikes
• Most negative sentiment comes from security-focused PRs (expected and appropriate)
• No extreme negative outliers - indicates balanced, professional tone

---

🏷️ Topic Analysis

Identified Discussion Topics

5 Major Topics Detected:

1. Topic 1 - MCP & Command Tools (28 items, 28%): Focus on tool integration, file handling, MCP servers, and command testing

   • Keywords: tool, file, mcp, command, test

2. Topic 0 - Data Structures & Testing (25 items, 25%): Updates to fields, functions, and test coverage

   • Keywords: update, field, function, test, output

3. Topic 2 - Error Handling & Quality (16 items, 16%): Error management, agent behavior, code quality

   • Keywords: set, error, work, agent, quality

4. Topic 3 - Feature Additions (17 items, 17%): New features, flags, validation support

   • Keywords: add, support, flag, output, validation

5. Topic 4 - Bug Fixes (14 items, 14%): Test fixes, pattern improvements

   • Keywords: fix, test, use, call, pattern

---

🔑 Keyword Trends

Most Important Technical Terms

Top 15 Keywords by TF-IDF Importance:

Rank	Keyword	TF-IDF	Category
1	footer	0.697	Documentation
2	assignee	0.492	GitHub Integration
3	actor	0.434	GitHub Context
4	schema	0.388	Data Validation
5	permission	0.343	Security
6	cli	0.335	Command-Line Interface
7	tool	0.332	Integration
8	flag	0.320	Configuration
9	call	0.316	Function Logic
10	helper	0.313	Code Structure

Top Phrase Patterns (Bigrams):

• "coding agent" (65 occurrences) - Frequent footer text in PR descriptions
• "safe output" (42 occurrences) - Major feature area
• "mcp server" (35 occurrences) - Active development focus
• "original prompt" (29 occurrences) - Prompt engineering mentions
• "error message" (20 occurrences) - Quality improvements

---

☁️ Topic Word Cloud

Visual Dominance: Terms like footer, assignee, actor, schema, tool, and mcp appear 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, and error appear 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:

• Secret scanning enhancements
• Vulnerability fixes (CWE-200, ReDoS)
• Permission validation

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 😊

1. PR #15240 (+0.353)
   "Add allowed-repos support to add-labels and close-issue safe"
   Focus: Feature enhancement with clear benefits

2. PR #15219 (+0.344)
   "Add unassign-from-user safe output handler"
   Focus: New capability addition

3. PR #15237 (+0.340)
   "Extract duplicate logic to safe output helper functions"
   Focus: Code quality improvement through refactoring

Most Negative PRs (Security-Focused) 🔒

1. PR #15231 (-0.195)
   "Reduce custom secret minimum length threshold from 8 to 6 characters"
   Context: Security configuration adjustment

2. PR #15233 (-0.172)
   "Fix secret prefix preservation vulnerability (CWE-200)"
   Context: CVE remediation

3. 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:

• Average PR description length: ~1,800 characters
• All PRs include: Problem statement, solution description, and context
• Footer consistency: 65 PRs include "coding agent" promotional footer
• Technical depth: Bodies are 144x more detailed than titles in sentiment expression

Engagement Pattern:

• All 50 PRs merged within 24 hours
• No extended discussion cycles (comment data unavailable)
• Fast merge indicates clear, well-structured proposals

---

💡 Recommendations

🎯 Continue Current Practices

1. Security-First Language: Maintain serious tone for security PRs - negative sentiment here is appropriate and signals proper prioritization

2. Structured PR Format: Current format (Problem → Solution → Context) produces clear, neutral-positive sentiment

3. Safe Outputs Development: This feature area shows high activity and positive reception - continue investment

⚠️ Areas to Monitor

1. Footer Repetition: The "coding agent minute survey" footer appears in 20+ PRs. Consider if this is adding value or creating noise.

2. Title Neutrality: Titles are perfectly neutral (-0.001 sentiment). Consider adding more descriptive language to help readers quickly understand PR impact.

3. Topic Balance: 28% of PRs focus on MCP/tools. Ensure other areas (documentation, testing, UX) also receive attention.

✨ Enhancement Opportunities

1. Highlight Benefits: PR bodies are more positive (+0.144) than titles. Consider including benefit statements in titles for better engagement.

2. Conversation Data: Future runs should capture PR comments/reviews for deeper analysis of interaction patterns.

3. Temporal Tracking: Store this analysis in cache memory to enable trend detection across multiple periods.

---

🔬 Methodology

NLP Techniques Applied:

• Sentiment Analysis: TextBlob polarity scoring (-1 to +1 scale)
• Topic Modeling: TF-IDF vectorization + K-means clustering (5 clusters)
• Keyword Extraction: TF-IDF with unigram, bigram, and trigram analysis
• Text Preprocessing: Tokenization, stopword removal, lemmatization

Data Processing:

• Cleaned markdown, code blocks, URLs, and special characters
• Removed common stopwords plus domain-specific terms (pr, github, workflow)
• Applied lemmatization for term normalization

Libraries Used:

• NLTK: Natural language processing and tokenization
• scikit-learn: Machine learning (TF-IDF, K-means)
• TextBlob: Sentiment polarity analysis
• WordCloud: Visual term frequency representation
• Pandas/NumPy: Data manipulation and analysis
• Matplotlib/Seaborn: Statistical visualizations

Data Sources:

• GitHub PR metadata (title, body, state, timestamps)
• 50 PRs merged in last 24 hours (Feb 12-13, 2026)
• Comment/review data not available in this run

---

📦 Artifacts

Generated Data Files:

• conversations.csv - Cleaned text data with sentiment scores
• keywords.csv - Top 20 keywords with TF-IDF scores
• topics.csv - 5 topic clusters with distributions
• pr_sentiment.csv - Per-PR sentiment aggregations
• summary.json - Complete analysis summary

Visualizations:

• Sentiment distribution histogram and category breakdown
• Topic cluster frequency analysis
• Top 15 keywords by TF-IDF importance
• Sentiment timeline across PR sequence
• Word cloud of most common terms

---

🔗 Workflow Details

• Repository: github/gh-aw
• Run ID: §21983143116
• Analysis Date: February 13, 2026, 10:29 UTC
• Workflow: Copilot PR Conversation NLP Analysis

AI generated by Copilot PR Conversation NLP Analysis

• expires on Feb 20, 2026, 10:34 AM UTC