Skill 4: Content Analysis — Stats, Sentiment & Keywords

Overview

Extracts actionable insights from any text: word counts, reading time, top keywords, sentiment score, and structure analysis.

Problem

Content teams need quick insights but manual analysis is slow. Existing tools are expensive or complex.

Solution

Lightweight analyzer that returns structured insights in <50ms.

Implementation

// analyzer.js
function analyzeContent(input) {
  const text = String(input).trim();
  const words = text.split(/\s+/).filter(w => w.length > 0);
  const sentences = text.split(/[.!?]+/).filter(s => s.trim().length > 0);
  const paragraphs = text.split(/\n\s*\n/).filter(p => p.trim().length > 0);
  
  // Word frequency (top 10, min 4 chars)
  const freq = {};
  for (const w of words) {
    const clean = w.toLowerCase().replace(/[^\w]/g, '');
    if (clean.length > 3) freq[clean] = (freq[clean] || 0) + 1;
  }
  const topWords = Object.entries(freq)
    .sort((a, b) => b[1] - a[1])
    .slice(0, 10)
    .map(([word, count]) => ({ word, count }));
  
  // Reading time (avg 200 wpm)
  const readingTimeMin = Math.ceil(words.length / 200);
  
  // Simple sentiment
  const positive = ['good', 'great', 'excellent', 'amazing', 'awesome', 'love', 'best', 'perfect', 'happy', 'success', 'win', 'great', 'fantastic', 'wonderful', 'brilliant'];
  const negative = ['bad', 'terrible', 'awful', 'hate', 'worst', 'fail', 'error', 'problem', 'issue', 'wrong', 'broken', 'poor', 'disappointing', 'frustrating', 'annoying'];
  let sentiment = 0;
  for (const w of words) {
    const lw = w.toLowerCase().replace(/[^\w]/g, '');
    if (positive.includes(lw)) sentiment++;
    if (negative.includes(lw)) sentiment--;
  }
  
  return {
    stats: {
      chars: text.length,
      words: words.length,
      sentences: sentences.length,
      paragraphs: paragraphs.length,
      reading_time_min: readingTimeMin
    },
    top_words: topWords,
    sentiment_score: sentiment,
    sentiment_label: sentiment > 2 ? 'positive' : sentiment < -2 ? 'negative' : 'neutral'
  };
}

// Usage
const result = analyzeContent("This is amazing! I love this product. It works great. Best purchase ever. Highly recommend!");
/*
{
  stats: { chars: 91, words: 15, sentences: 5, paragraphs: 1, reading_time_min: 1 },
  top_words: [{word:"this",count:2},{word:"amazing",count:1},...],
  sentiment_score: 4,
  sentiment_label: "positive"
}
*/

Batch Analysis

// analyze-batch.js
const fs = require('fs');

function analyzeDirectory(dir) {
  const results = [];
  fs.readdirSync(dir).forEach(file => {
    if (file.endsWith('.md') || file.endsWith('.txt')) {
      const text = fs.readFileSync(`${dir}/${file}`, 'utf8');
      const analysis = analyzeContent(text);
      results.push({ file, ...analysis });
    }
  });
  return results;
}

// Generate report
const report = analyzeDirectory('./blog-posts');
console.table(report.map(r => ({
  file: r.file,
  words: r.stats.words,
  sentiment: r.sentiment_label,
  top_word: r.top_words[0]?.word
})));

x402 Pay-Per-Analyze

curl -X POST https://your-api.com/api/analyze \
  -H "Content-Type: application/json" \
  -H "X-PAYMENT: <base64_payment>" \
  -d '{"text":"This is amazing! I love this product. It works great. Best purchase ever. Highly recommend!"}'
# Returns: {"stats":{...},"top_words":[...],"sentiment_score":4,"sentiment_label":"positive","agent":"loki"}

Use Cases

Customization

// Add domain-specific sentiment words
const domainSentiment = {
  positive: ['conversion', 'retention', 'engagement', 'growth', 'revenue', 'roi'],
  negative: ['churn', 'bounce', 'drop', 'decline', 'loss', 'bug', 'downtime']
};

// Add entity extraction
function extractEntities(text) {
  const entities = {
    emails: text.match(/[\w.+-]+@[\w-]+\.[\w.-]+/g) || [],
    urls: text.match(/https?:\/\/[^\s]+/g) || [],
    mentions: text.match(/@\w+/g) || [],
    hashtags: text.match(/#\w+/g) || [],
    currency: text.match(/\$\d+(?:,\d{3})*(?:\.\d{2})?/g) || []
  };
  return entities;
}

Value

• Speed: <50ms per analysis
• Cost: $0.003 per call via x402
• No API keys: Self-contained, no external dependencies
• Extensible: Add custom dictionaries, entities, metrics