Keyword Density Checker

The Keyword Density Checker analyzes any text and reports how often each word and multi-word phrase appears, along with its percentage of the total content. This is a core SEO task: you want your target keywords to appear often enough to signal topic relevance to search engines, but not so often that it reads as keyword stuffing. The sweet spot is typically 1-3% for primary keywords in natural-sounding copy.

Paste an article, blog post, product description, or any text and the tool counts words, excludes stopwords (the, a, an, and, etc. by default), shows the top single words, bigrams (two-word phrases), and trigrams (three-word phrases) by frequency, and calculates density percentages. Identify whether your primary keyword is present enough, discover supporting keywords you mention heavily without planning to, and spot over-optimized content that may trigger Google\u2019s keyword-stuffing penalty. All analysis runs in your browser, so confidential draft content stays on your machine.

Keyword density is the percentage of total words that match a specific keyword. For a 1000-word article where \"dogs\" appears 15 times: density = 15/1000 = 1.5%.

For phrases (n-grams), the calculation is: count of the phrase / total number of n-grams. A 1000-word text has about 999 bigrams (sliding window of 2), so a bigram appearing 10 times has density ~1%.

Analysis steps:
1. Tokenize the text into words using Unicode word boundaries
2. Apply lowercase for case-insensitive counting
3. Remove punctuation attached to words
4. Optionally remove stopwords (common words like \"the\", \"a\", \"an\", \"and\", \"or\") that are usually excluded from meaningful keyword analysis
5. Count each word\u2019s frequency
6. For phrases, build n-grams (pairs, triples) and count their frequencies
7. Sort by frequency descending
8. Calculate percentages

Stopword list: defaults to ~170 English stopwords. Customizable if you want to include or exclude specific words from counting. Other language stopword lists available (Spanish, German, French).

Stemming: optional. Converts \"run\", \"running\", \"ran\" to one stem so they count together. Uses Porter Stemmer. Useful for broader topic analysis but can over-merge unrelated words.

Ideal keyword density ranges (general SEO consensus, not a hard rule):
- Primary keyword: 0.5-2.5% (some say up to 3%)
- Secondary keywords: 0.5-1%
- Over 3-5% risks appearing spammy

Modern SEO (post-BERT, post-Helpful Content Update) emphasizes topical relevance and semantic richness over keyword density. A page should naturally mention keywords as part of covering the topic thoroughly, not hit a specific density target.

TF-IDF (Term Frequency-Inverse Document Frequency) is a more sophisticated measure that considers how distinctive a word is across a corpus. This tool reports raw frequencies; for TF-IDF you need a reference corpus.

Common keyword-stuffing signals Google detects:
- Unnaturally high density of exact-match keywords
- Over-optimization of anchor text
- Hidden text containing keywords
- Repetitive, thin content focused on keywords rather than value

Compared to paid SEO tools (Surfer, Clearscope, Frase), this tool provides basic density and frequency analysis without the subscription. Paid tools add competitor benchmarking, topic modeling, and content briefs; this tool is the starting point.

Compared to spreadsheet word counting, this tool handles stopwords, stemming, and n-gram analysis out of the box. Spreadsheets require more setup but offer flexibility for custom analysis.

Compared to Python NLTK or similar libraries, this tool is interactive and requires no setup. For batch analysis and programmatic use, libraries are better; for quick checks, this tool wins.