Why is my Keyword Density & Frequency Checker miscalculating specific content optimization term frequencies?

• Tokenization Strategy for Multi-Word Phrases (N-grams): Your tool likely tokenizes the text into individual words. For a phrase like "content optimization" to be counted correctly, your system must either:
  1. Generate N-grams (specifically bi-grams in this case) from the tokenized corpus *before* frequency counting, and then match the target phrase against these N-grams.
  2. Implement a specific search pattern that looks for the exact sequence of words, respecting word boundaries, rather than just summing individual word occurrences. If it's breaking "content optimization" into "content" and "optimization" and then merely trying to reconstruct, it's prone to error.
  Ensure your tokenizer isn't inadvertently splitting the phrase or treating it as two separate tokens that are later re-joined without strict contiguity checks.
• Text Normalization Inconsistencies:
  • Case Sensitivity: Verify that all text is consistently converted to a single case (e.g., lowercase) before comparison. "Content Optimization" and "content optimization" are distinct if case isn't normalized.
  • Punctuation and Special Characters: Check how punctuation is handled. A phrase like "content-optimization" or "content, optimization" might be treated differently from "content optimization" if punctuation removal isn't precise or if it's too aggressive.
  • Unicode Normalization: While less common for standard English, different Unicode forms (NFC vs. NFD) can cause subtle mismatches if the target keyword and the corpus text have different normalizations. This is more relevant for accented characters or complex scripts.
  • Invisible Characters: Look for non-breaking spaces (  or \xa0) or zero-width spaces. These visually appear as regular spaces but are distinct characters that can break a multi-word phrase match.
• Stemming/Lemmatization Interference: If your checker applies stemming or lemmatization, it's highly likely to be the culprit. These processes reduce words to their root form (e.g., "optimization" to "optimize"). While beneficial for single-word text analysis and identifying thematic relevance (useful for semantic SEO), they will prevent an exact match for the phrase "content optimization" if applied before or during the phrase matching step. For exact phrase frequency, stemming and lemmatization should be bypassed or disabled.
• Regex Pattern Precision: If you are using regular expressions to find the phrase, ensure the pattern correctly accounts for word boundaries (e.g., \bcontent\s+optimization\b) and is not too greedy or too restrictive. A simple substring search might count "recontent optimization" or miss variations.
• Pre-processing Pipeline Order: Review the exact sequence of operations applied to the raw text. Sometimes, stop word removal or other filtering steps might inadvertently modify or remove parts of your target phrase before the frequency count, leading to an undercount.

You were totally right about the stemming/lemmatization interference! Once I bypassed that for the multi-word phrases, the counts are spot on now. Ngl, tho, now I'm seeing weird behavior with hyphenated terms like "e-commerce optimization" – seems like it's treating "e-commerce" as two words sometimes even with a specific regex.