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Kunthawat Greethong
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skills/seo-analyzers/scripts/thai_keyword_analyzer.py
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270
skills/seo-analyzers/scripts/thai_keyword_analyzer.py
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#!/usr/bin/env python3
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"""
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Thai Keyword Analyzer
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Analyze keyword density in Thai text with PyThaiNLP integration.
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Handles Thai language specifics (no spaces between words).
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"""
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import argparse
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import json
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import sys
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from typing import Dict, List, Optional
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try:
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from pythainlp import word_tokenize
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from pythainlp.util import normalize
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THAI_SUPPORT = True
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except ImportError:
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THAI_SUPPORT = False
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print("Warning: PyThaiNLP not installed. Install with: pip install pythainlp")
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class ThaiKeywordAnalyzer:
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"""Analyze keyword density in Thai text"""
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def __init__(self):
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self.thai_stopwords = set([
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'และ', 'หรือ', 'แต่', 'ว่า', 'ถ้า', 'หาก', 'ซึ่ง', 'ที่', 'ใน', 'บน',
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'ใต้', 'เหนือ', 'จาก', 'ถึง', 'ที่', 'การ', 'ความ', 'อย่าง', 'เมื่อ',
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'สำหรับ', 'กับ', 'ของ', 'เป็น', 'อยู่', 'คือ', 'ได้', 'ให้', 'ไป', 'มา'
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])
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def count_words(self, text: str) -> int:
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"""Count Thai words accurately"""
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if not THAI_SUPPORT:
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return len(text.split())
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tokens = word_tokenize(text, engine="newmm")
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return len([t for t in tokens if t.strip() and not t.isspace()])
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def calculate_density(self, text: str, keyword: str) -> float:
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"""Calculate keyword density"""
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if not THAI_SUPPORT:
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text_words = text.lower().split()
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keyword_count = text.lower().count(keyword.lower())
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return (keyword_count / len(text_words) * 100) if text_words else 0
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text_norm = normalize(text)
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keyword_norm = normalize(keyword)
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count = text_norm.count(keyword_norm)
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word_count = self.count_words(text)
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return (count / word_count * 100) if word_count > 0 else 0
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def find_positions(self, text: str, keyword: str) -> List[int]:
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"""Find all keyword positions"""
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positions = []
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text_lower = text.lower()
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keyword_lower = keyword.lower()
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start = 0
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while True:
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pos = text_lower.find(keyword_lower, start)
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if pos == -1:
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break
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positions.append(pos)
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start = pos + 1
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return positions
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def check_critical_placements(self, text: str, keyword: str) -> Dict:
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"""Check keyword in critical locations"""
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text_lower = text.lower()
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keyword_lower = keyword.lower()
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# First 200 chars (approximately first 100 Thai words)
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in_first_100_words = keyword_lower in text_lower[:200]
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# Check H1 (first line if it starts with #)
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lines = text.split('\n')
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in_h1 = False
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if lines and lines[0].startswith('#'):
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in_h1 = keyword_lower in lines[0].lower()
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# Last 500 chars (approximately conclusion)
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in_conclusion = keyword_lower in text_lower[-500:] if len(text) > 500 else False
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# Count H2 occurrences
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h2_count = sum(1 for line in lines if line.startswith('##') and keyword_lower in line.lower())
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return {
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'in_first_100_words': in_first_100_words,
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'in_h1': in_h1,
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'in_conclusion': in_conclusion,
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'in_h2_count': h2_count
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}
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def detect_stuffing(self, text: str, keyword: str, density: float) -> Dict:
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"""Detect keyword stuffing risk"""
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risk_level = "none"
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warnings = []
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if density > 3.0:
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risk_level = "high"
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warnings.append(f"Keyword density {density:.1f}% is very high (over 3%)")
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elif density > 2.5:
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risk_level = "medium"
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warnings.append(f"Keyword density {density:.1f}% is high (over 2.5%)")
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# Check for clustering in paragraphs
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paragraphs = text.split('\n\n')
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for i, para in enumerate(paragraphs[:10]): # Check first 10 paragraphs
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para_density = self.calculate_density(para, keyword)
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if para_density > 5.0:
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risk_level = "high" if risk_level != "high" else risk_level
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warnings.append(f"Paragraph {i+1} has very high density ({para_density:.1f}%)")
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return {
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'risk_level': risk_level,
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'warnings': warnings,
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'safe': risk_level in ["none", "low"]
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}
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def get_density_status(self, density: float, language: str = 'th') -> str:
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"""Determine if density is appropriate"""
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if language == 'th':
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# Thai target: 1.0-1.5%
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if density < 0.5:
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return "too_low"
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elif density < 1.0:
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return "slightly_low"
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elif density <= 1.5:
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return "optimal"
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elif density <= 2.0:
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return "slightly_high"
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else:
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return "too_high"
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else:
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# English target: 1.5-2.0%
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if density < 1.0:
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return "too_low"
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elif density < 1.5:
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return "slightly_low"
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elif density <= 2.0:
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return "optimal"
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elif density <= 2.5:
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return "slightly_high"
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else:
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return "too_high"
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def get_recommendations(self, density: float, placements: Dict, language: str = 'th') -> List[str]:
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"""Generate recommendations"""
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recs = []
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if language == 'th':
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if density < 1.0:
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recs.append("เพิ่มการใช้คำหลักในเนื้อหา (target: 1.0-1.5%)")
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elif density > 2.0:
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recs.append("ลดการใช้คำหลักลง อาจถูกมองว่า keyword stuffing")
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if not placements['in_first_100_words']:
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recs.append("เพิ่มคำหลักในย่อหน้าแรก (100 คำแรก)")
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if not placements['in_h1']:
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recs.append("เพิ่มคำหลักในหัวข้อหลัก (H1)")
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if not placements['in_conclusion']:
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recs.append("เพิ่มคำหลักในบทสรุป")
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if placements['in_h2_count'] < 2:
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recs.append("เพิ่มคำหลักในหัวข้อรอง (H2) อย่างน้อย 2-3 แห่ง")
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else:
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if density < 1.5:
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recs.append("Increase keyword usage (target: 1.5-2.0%)")
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elif density > 2.5:
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recs.append("Reduce keyword usage to avoid stuffing penalty")
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if not placements['in_first_100_words']:
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recs.append("Add keyword in first 100 words")
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if not placements['in_h1']:
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recs.append("Add keyword in H1 headline")
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if not placements['in_conclusion']:
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recs.append("Add keyword in conclusion")
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return recs
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def analyze(self, text: str, keyword: str, language: str = 'th') -> Dict:
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"""Full keyword analysis"""
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word_count = self.count_words(text)
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density = self.calculate_density(text, keyword)
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positions = self.find_positions(text, keyword)
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placements = self.check_critical_placements(text, keyword)
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stuffing = self.detect_stuffing(text, keyword, density)
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status = self.get_density_status(density, language)
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recommendations = self.get_recommendations(density, placements, language)
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return {
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'word_count': word_count,
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'keyword': keyword,
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'occurrences': len(positions),
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'density': round(density, 2),
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'target_density': '1.0-1.5%' if language == 'th' else '1.5-2.0%',
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'status': status,
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'critical_placements': placements,
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'keyword_stuffing_risk': stuffing['risk_level'],
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'recommendations': recommendations
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}
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def main():
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"""Main entry point"""
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parser = argparse.ArgumentParser(
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description='Analyze keyword density in Thai or English text'
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)
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parser.add_argument(
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'--text', '-t',
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required=True,
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help='Text content to analyze'
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)
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parser.add_argument(
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'--keyword', '-k',
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required=True,
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help='Target keyword'
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)
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parser.add_argument(
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'--language', '-l',
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choices=['th', 'en'],
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default='th',
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help='Content language (default: th)'
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)
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parser.add_argument(
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'--output', '-o',
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choices=['json', 'text'],
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default='text',
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help='Output format (default: text)'
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)
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args = parser.parse_args()
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# Analyze
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analyzer = ThaiKeywordAnalyzer()
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result = analyzer.analyze(args.text, args.keyword, args.language)
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# Output
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if args.output == 'json':
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print(json.dumps(result, indent=2, ensure_ascii=False))
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else:
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print("\n📊 Keyword Analysis Results\n")
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print(f"Keyword: {result['keyword']}")
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print(f"Word Count: {result['word_count']}")
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print(f"Occurrences: {result['occurrences']}")
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print(f"Density: {result['density']}% (target: {result['target_density']})")
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print(f"Status: {result['status']}")
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print(f"\nCritical Placements:")
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print(f" ✓ First 100 words: {'Yes' if result['critical_placements']['in_first_100_words'] else 'No'}")
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print(f" ✓ H1 Headline: {'Yes' if result['critical_placements']['in_h1'] else 'No'}")
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print(f" ✓ Conclusion: {'Yes' if result['critical_placements']['in_conclusion'] else 'No'}")
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print(f" ✓ H2 Headings: {result['critical_placements']['in_h2_count']} found")
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print(f"\nKeyword Stuffing Risk: {result['keyword_stuffing_risk']}")
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if result['recommendations']:
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print(f"\n💡 Recommendations:")
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for rec in result['recommendations']:
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print(f" • {rec}")
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print()
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if __name__ == '__main__':
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main()
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