Our on-page SEO checker broke

Hello Mia Brown, Ugh, I totally get the frustration here. There's nothing quite like a tool that's supposed to help with precision, only to give you wildly inconsistent data. It's like trying to hit a moving target with a broken scope. First off, just a tiny nitpick – you said 'its like it miscounts' when I think you meant 'it's like it miscounts.' Easy typo to make, and honestly, less frustrating than a broken keyword counter, right? But seriously, this kind of issue with a core content analysis tool can really throw a wrench into your entire content optimization strategy. I've faced similar headaches with tracking campaign performance where a minor data discrepancy scaled into a major reporting nightmare. Your description points directly to common pitfalls in text preprocessing, which is the unsung hero (or villain) of any robust keyword density and frequency tool. The core problem usually isn't the counting itself, but what you're actually telling the counter to count. Here’s a breakdown of areas to meticulously review, going beyond what you've already tried, and some actionable steps:

1. Aggressive & Consistent Text Normalization:

This is likely where your "content" example is failing. "content." and "content" are treated as two different tokens. You need a multi-stage normalization process:

• Lowercase Conversion: Convert the entire text to lowercase. "SEO", "seo", "Seo" should all be treated as "seo".
• Punctuation Removal/Replacement: Strip out all punctuation. A simple regex like [^\w\s] (anything that's not a word character or whitespace) often works, but be careful with contractions (e.g., "don't" might become "dont"). For keyword density, it's usually best to remove it entirely or replace it with a space.
• Diacritic Removal: For multilingual support, handle diacritics (e.g., accents like in 'café' vs 'cafe'). Python's unicodedata.normalize('NFKD', text).encode('ascii', 'ignore').decode('utf-8') is a common approach to convert accented characters to their base ASCII equivalents.
• Whitespace Normalization: Replace multiple spaces, tabs, and newlines with a single space. This prevents empty tokens or miscounts from formatting issues.

2. Robust Tokenization Strategy:

Once your text is clean, how you break it into "words" (tokens) is critical. Simple text.split() on whitespace is often insufficient, especially with hyphenated words, contractions, or numbers. Consider these:

• Regex-based Tokenization: Instead of just splitting, use a regex to *find* words. A pattern like \b\w+\b (matches word boundaries and one or more word characters) is more precise than just splitting. However, be aware of how it handles numbers or mixed alphanumeric strings if they are relevant keywords.
• Advanced NLP Libraries: For true bulletproof accuracy, especially across languages, consider integrating established NLP libraries.
  • Python: NLTK (Natural Language Toolkit) or SpaCy offer highly optimized and tested tokenizers. NLTK's word_tokenize is excellent for general English.
  • JavaScript: Libraries like Natural can provide similar functionality.
  These libraries account for many linguistic nuances that custom regex might miss or oversimplify.

3. Handling Edge Cases & Special Characters:

• Hyphenated Words: Decide if "on-page" should be one keyword or "on" and "page". Your normalization/tokenization strategy needs to consistently reflect this.
• Contractions: "it's" -> "its" (after apostrophe removal) or "it" and "is"? Most keyword density tools would strip the apostrophe and treat it as "its" or simply "it".
• Numeric Keywords: If "2023" or "5G" are relevant keywords, ensure your word extraction regex includes numbers.
• Empty Tokens: After all cleaning, ensure you filter out any empty strings that might have resulted from aggressive punctuation removal.

4. Re-evaluating Your Example:

Input Text: "The best SEO tips include keyword research and content optimization. For great SEO, focus on content."

If you apply aggressive normalization:

1. Lowercase: "the best seo tips include keyword research and content optimization. for great seo, focus on content."
2. Punctuation Removal: "the best seo tips include keyword research and content optimization for great seo focus on content"
3. Tokenization (e.g., splitting by space): ["the", "best", "seo", "tips", "include", "keyword", "research", "and", "content", "optimization", "for", "great", "seo", "focus", "on", "content"]

Now, "content" appears 3 times. This should resolve your specific example issue. Your initial regex might be too conservative, perhaps not considering punctuation as a word delimiter that should be removed *before* counting. For robust search engine ranking factors, having an accurate count is non-negotiable.

5. Unit Testing with a Diverse Corpus:

You mentioned testing with various inputs, but ensure your test suite specifically includes:

• Texts with heavy punctuation and special characters.
• Texts from different languages (even if you only support English, these reveal encoding issues).
• Very long articles to test performance and edge cases related to text size.
• Texts with hyphenated words, contractions, and numbers.

By focusing on a more rigorous, multi-step text preprocessing pipeline, you should be able to achieve the "bulletproof accuracy" you're aiming for. It's a common hurdle, but once you nail the text preparation, the counting itself becomes trivial.

Hope this helps your conversions and keeps your users happy!