Overview

The semanticAnalyzer.js performs semantic analysis on posts including sentiment, relevance, and content quality assessment. With 30.83% coverage, much of the semantic analysis pipeline is untested.

Current Coverage

• Statements: 30.83%
• Branches: 75.00%
• Functions: 20.00%
• Lines: 30.83%
• Target: 100% coverage

Uncovered Areas

Major untested sections:

• Sentiment analysis
• Relevance scoring
• Content quality assessment
• Semantic similarity calculations
• LLM-based analysis
• Cache management
• Batch analysis
• Analysis fallbacks

Key Functionality to Test

1. Sentiment Analysis

• Detecting positive sentiment
• Detecting negative sentiment
• Detecting neutral sentiment
• Mixed sentiment handling
• Sentiment scoring ranges

2. Relevance Scoring

• Content relevance to topics
• User relevance to content
• Context-based relevance
• Relevance thresholds

3. Quality Assessment

• Post quality metrics
• Content depth analysis
• Spam detection
• Bot detection
• Quality normalization

4. LLM Integration

• LLM-based analysis when enabled
• Fallback to rule-based analysis
• Prompt construction
• Response parsing
• Error handling

Testing Strategy

describe('SemanticAnalyzer', () => {
  describe('Initialization', () => {
    test('initializes with LLM enabled');
    test('initializes with LLM disabled');
    test('configures cache settings');
  });

  describe('Sentiment Analysis', () => {
    test('detects positive sentiment');
    test('detects negative sentiment');
    test('detects neutral sentiment');
    test('handles mixed sentiment');
    test('scores within valid range');
  });

  describe('Relevance Scoring', () => {
    test('scores topic relevance');
    test('scores user relevance');
    test('considers context');
    test('respects thresholds');
  });

  describe('Quality Assessment', () => {
    test('assesses post quality');
    test('detects spam');
    test('detects bot content');
    test('normalizes quality scores');
  });

  describe('LLM Integration', () => {
    test('uses LLM when enabled');
    test('falls back to rules when disabled');
    test('constructs valid prompts');
    test('parses LLM responses');
    test('handles LLM errors');
  });

  describe('Cache Management', () => {
    test('caches analysis results');
    test('retrieves from cache');
    test('respects cache TTL');
    test('handles cache misses');
  });
});

Test Fixtures Needed

• Sample posts with various sentiments
• High/low quality content examples
• Spam and bot content samples
• Mock LLM responses
• Cache scenarios

Acceptance Criteria

• Sentiment analysis fully tested
• Relevance scoring verified
• Quality assessment covered
• LLM integration tested (enabled/disabled)
• Cache management verified
• Error handling covered
• Overall coverage 100%

Related

• Parent: Increase plugin-nostr test coverage to 100% #39 - Increase plugin-nostr test coverage to 100%
• Related: lib/contextAccumulator.js - Uses sentiment data
• Related: lib/discovery.js - Uses quality scores

Priority

🟡 MEDIUM - Important for content quality filtering and discovery.