What is keyword density and what is the ideal target?

Keyword density is the percentage of times a target keyword appears in a piece of content relative to the total word count. The classic SEO rule of thumb is 1-2% density for the primary keyword, though Google has not used keyword density as a direct ranking factor since around 2013, when semantic search via Hummingbird and BERT replaced literal keyword matching. Modern SEO focuses on topical relevance and natural language patterns rather than density. However, keyword frequency remains useful for content auditing: under-mentioning the keyword (under 0.5%) suggests the page is not focused enough on the topic; over-mentioning (over 3%) signals keyword stuffing, which Google's spam algorithms penalize. The healthy range is 0.8-2.5% for the primary keyword across a 1500-2500 word post. Equally important: cover semantic variants (synonyms, related terms, entities) to demonstrate topical depth. A 2025 SurferSEO study of 50,000 SERPs found that top-ranking pages had primary keyword density of 1.3% on average, but secondary keyword and entity coverage varied widely with topic — comprehensiveness mattered more than density.

What is TF-IDF and how does it apply to SEO?

TF-IDF (Term Frequency-Inverse Document Frequency) is an information retrieval technique that scores how important a word is to a document relative to a corpus. TF measures how often a word appears in your document; IDF measures how rare that word is across all documents in the corpus. The product (TF * IDF) is high for words that are common in your document but rare across the corpus — precisely the words that define your document's topic. SEO tools like Clearscope, MarketMuse, and SurferSEO use TF-IDF to compare your draft against top-ranking competitors' content for the same keyword and identify terms competitors use that you do not. The output is a list of suggested terms to add (or remove) to bring your content in line with what Google expects for the topic. TF-IDF is not a ranking factor itself, but it is a useful proxy for topical depth. Google has confirmed using more sophisticated NLP models (BERT, MUM) since 2019, which go beyond TF-IDF, but the underlying principle — measuring topic coverage — remains valid.

How do I avoid keyword stuffing?

Keyword stuffing is the practice of repeating a keyword or its variants unnaturally to manipulate rankings. Google's algorithms reliably detect it via several signals: keyword density above 3-5% for short-tail keywords, repeated phrases ending sentences (especially identical anchor text), invisible text or text styled to blend into background, and grammatically awkward sentences forced to include the keyword. Penalties range from ranking suppression to full deindexing. Detection: run this frequency counter on your draft and check if the primary keyword density exceeds 2.5%. If yes, replace some instances with: synonyms, pronouns ("it", "this approach"), conceptual paraphrases ("the technique above"), or simply remove redundant mentions. Aim for natural language: read your draft aloud; if any sentence sounds awkward because the keyword is forced, rewrite. A modern SEO best practice: forget about hitting exact density numbers and focus on topical comprehensiveness. Cover the topic, mention the keyword where natural, and let semantic search handle the rest.

How is word frequency analysis different from a content audit?

Word frequency analysis is one input to a content audit. The audit is a broader process: review existing content for SEO performance (rankings, traffic, conversions), identify thin or outdated pages, find content gaps where competitors rank but you do not, evaluate technical issues (broken links, slow load), and produce a prioritized list of pages to update, consolidate, or delete. Word frequency analysis fits inside the audit by surfacing which words your existing content uses heavily and which it under-uses. For example, if you have 50 blog posts about "email marketing" but the term "deliverability" appears only 3 times across all posts, you have a content gap. Run frequency analysis on your top 10 ranking competitors' content to identify terms they use that you should cover. Use the output as a topic checklist for new posts and an editorial guide for refreshing existing posts. Tools like Frase and SurferSEO automate this comparison; this tool does the manual frequency count, which you then compare against competitor outputs.

What are n-grams and why do they matter for SEO?

An n-gram is a sequence of n consecutive words from a text. Unigrams are single words ("seo"), bigrams are two-word phrases ("seo audit"), trigrams are three-word phrases ("seo audit checklist"), and so on. For SEO, n-grams matter because: (1) most search queries are 2-4 words, so optimizing for unigrams alone misses the longer-tail phrases users actually type, (2) bigrams and trigrams reveal content topic clusters more accurately than single-word counts (a post mentioning "running shoes" 30 times is clearly about running shoes, even if "shoes" alone appears 50 times), and (3) Google's algorithm understands phrases (entities, named entities) as semantic units, not just bags of words. Run n-gram frequency analysis to identify your top phrases (3-5 word sequences appearing 5+ times across the document) and ensure those align with target keywords. Tools like Google's Natural Language API expose n-grams and entities directly; manual analysis works for shorter content. Most SEO content audit tools include n-gram analysis as standard.

Should I remove stop words from frequency analysis?

Yes, for SEO purposes. Stop words ("the", "a", "and", "of", "to", "is") are the most frequent words in any English text, but they carry no topical signal. If you analyze raw word frequency on a typical blog post, the top 20 words will be entirely stop words, which tells you nothing useful. Strip stop words before analysis to reveal the topically meaningful words underneath. This tool offers a stop-word filter toggle. The standard English stop word list (used in NLP libraries like NLTK and spaCy) contains 150-180 words; for SEO purposes, a shorter list of 50-70 words is sufficient. For different languages, use language-specific lists — German, Spanish, and French each have unique high-frequency function words. Note that for some queries, stop words matter: searches like "to be or not to be" depend on stop words. But for content audit and topic clustering, always strip them. Some tools also offer stemming (reducing "running", "runs", "ran" to "run") for additional accuracy.

How does Google's BERT and MUM affect keyword strategy?

BERT (Bidirectional Encoder Representations from Transformers, 2019) and MUM (Multitask Unified Model, 2021) are Google's natural language understanding models that go beyond keyword matching to understand search intent, context, and semantic relationships. Practical implication: Google can rank a page for "best laptops for video editing" even if the page never uses that exact phrase, as long as it covers the topic comprehensively (mentioning RAM, GPU, screen calibration, video software compatibility). This means keyword density and exact-match phrasing matter less than topical depth. However, primary keywords still matter: BERT confirms intent based on the words used, so a page targeting "running shoes" should still mention "running shoes" in title, H1, and naturally throughout content. The strategy: use the primary keyword in clear, natural ways (title, H1, opening paragraph, sub-headings as relevant), then write comprehensively about the topic without forcing keyword repetition. Frequency analysis still helps audit topic coverage; it just no longer dictates exact phrasing.

How can I find keyword opportunities by analyzing competitor content?

Process: identify your top 10 competitors for a target keyword (top-ranking pages in SERPs), download or copy their content into a frequency counter, generate top 50-100 words/phrases for each, and combine into a master list. Use this tool for individual analysis or a tool like SurferSEO/Clearscope for automated multi-competitor comparison. Then review your existing content (or planned outline) against the master list. Words that appear frequently in competitors but not in your content are gaps to fill. Words that appear frequently in your content but not in competitors are either differentiators or off-topic — investigate which. Look for: bigrams and trigrams that signal sub-topics (e.g., "user research methods" suggests a section on methodology), entities like product names, brands, and person names that competitors cite, and technical terms that establish topical authority. Aim for your content to use 70-80% of the high-frequency terms competitors use, plus 10-20% unique terms that differentiate.

What is over-optimization and how does it relate to keyword frequency?

Over-optimization is the practice of stuffing too many SEO signals into a page — exact-match anchor text, keyword in every sub-heading, repeated keyword in alt text, exact keyword in URL, meta description, and title — to the point that the page reads as algorithm-targeted rather than user-targeted. Google's 2012 Penguin update and ongoing spam algorithms penalize over-optimized pages. Symptoms: keyword density above 3% for the primary keyword, identical anchor text linking to the page from internal links, every H2 containing the same keyword variant, and unnatural keyword variations like "best running shoes for runners running marathons". Detection via this frequency counter: top word/phrase exceeds 3% of total words for non-stop-words. Fix: replace some keyword mentions with synonyms, pronouns, or conceptual references. Rewrite repetitive sub-headings to add variety. Use natural anchor text on internal links ("our running shoes guide" instead of literal keyword). Aim for a balance: clear topic targeting without algorithmic obsession.

How do I use frequency analysis to improve content readability?

Frequency analysis surfaces overused words and phrases that may make your writing repetitive. Beyond SEO keywords, look for: filler words ("just", "very", "really", "actually") that signal weak writing — Hemingway-style cuts these aggressively, transition words ("however", "therefore", "furthermore") used too often (more than 1 per 200 words feels academic), specific verbs or adjectives repeated within close proximity (a 1500-word post should not use "leverage" 10 times), and brand mentions or self-references over-prominent (saying "we" or your brand name more than necessary). After identifying overused words, use a thesaurus or rephrase to add variety. Note: not every overused word is bad — domain-specific terms in technical writing are unavoidably frequent. The frequency counter is a calibration tool, not a rule. Use judgment: if a word appears 50 times in a 2000-word post and is the topic of the post, that is fine; if it is a filler word, cut.

How accurate is automatic word frequency analysis vs manual?

Automatic analysis is fast and consistent; manual analysis is slower but catches nuance. Automatic counters split on whitespace and punctuation, normalize case, and produce a frequency table in seconds. They handle volume well — analyzing a 10,000-word document is instant. Manual review is better for: identifying contextual usage (the same word may appear in different meanings — "bank" as financial institution vs river bank), recognizing entities and proper nouns (a name appearing 20 times signals topical importance, but automatic counters treat it as just another word), and judging whether high frequency is appropriate for the topic. Best practice: run automatic analysis first to surface raw frequencies, then manually review the top 20-30 entries to interpret meaning. For multilingual content, also use language detection because frequency norms differ by language (Mandarin and Japanese require different tokenizers altogether). For very long content (50,000+ words), only automatic analysis is practical. This tool defaults to fast automatic analysis with stop word filtering.

Can I use word frequency analysis for non-SEO purposes?

Yes. Use cases beyond SEO: academic research (analyzing the rhetoric of historical speeches, comparing word usage across authors), marketing copy review (finding overused brand jargon and replacing with customer-friendly language), customer support analysis (running frequency on a year of support tickets to identify the most common issues), product review mining (analyzing reviews to extract recurring positive and negative themes), legal document review (identifying recurring contractual clauses or terms), social media monitoring (extracting trending topics from comment threads), book and content editing (catching repetition across long manuscripts), and language learning (identifying the most common words in a target language for prioritized study). Tools like Voyant Tools and AntConc offer advanced linguistic analysis beyond simple frequency. For SEO-specific use, this tool focuses on the metrics most relevant to content optimization, but the underlying frequency data has broad applicability across content analysis fields.

Free Word Frequency Counter

Find most frequent words and phrases in any text. Identify top keywords for SEO. Free, instant, accurate.

4.6on G2

4.8on Trustpilot

Used by 25,000+ marketers

Remove common stop words

What this tool does

Word Frequency Counter delivers fast, reliable results for find most frequent words and phrases in any text. identify top keywords for seo..

Designed to fit into your existing SEO and content workflow with no setup overhead.

How to use it

Five steps.

Paste your content into the analyzer

Drop in a blog post, product description, transcript, or any text. The tool tokenizes the input automatically.

Apply the stop word filter

Toggle off common function words (the, and, of, is) so the frequency table surfaces topically meaningful words.

Review the top 20-50 frequencies

Examine which words dominate. Compare against your target keyword density (1-2.5%) and look for over-optimization.

Run the same analysis on competitor content

Paste top-ranking competitor pages, generate frequency tables, compare against yours to find topic gaps.

Adjust your draft based on findings

Add missing topical terms competitors use, replace over-mentioned keywords with synonyms, and tighten repetitive language.

When teams use it

Six common workflows.

Content optimization audit

Run frequency analysis on top-ranking competitors and your draft to identify keyword and topic gaps before publishing.

Avoiding keyword stuffing

Check that no single word exceeds 2-3% density. Identify over-mentioned phrases and replace with synonyms or pronouns.

Editorial style review

Surface overused filler words (just, really, very) and transitions. Editors trim repetitive language to tighten copy.

Customer feedback theme extraction

Run frequency on a year of support tickets or product reviews to identify the most common issues and feedback themes.

Academic and research analysis

Analyze speeches, articles, or document corpora for linguistic patterns, author signatures, or rhetorical strategies.

Brand voice consistency

Compare frequency profiles across product, marketing, and support content to ensure consistent tone and terminology.

Platform guides

Integrate with major platforms.

Manual paste from any CMS

Open your post in WordPress, Ghost, Webflow, or any editor.
Switch to plain text view (or use Ctrl+A then copy from rendered preview).
Paste into this tool and click Analyze.
Apply stop-word filter and review the top 20-50 frequencies.

Google Docs

Select all text (Cmd+A or Ctrl+A) and copy.
Paste into this tool. Google Docs preserves rich formatting; the tool strips it before analysis.
For multi-document analysis, run each separately and combine top-N lists in a spreadsheet.
Use Tools > Word count for total word count baseline.

Notion

Open the page, click into the body, select all (Cmd+A).
Copy and paste into this tool.
For nested pages, copy each child page separately.
Notion's native word count (under three-dot menu) gives total only; use this tool for frequency.

Programmatic via Node.js or Python

For batch analysis, use Node.js with natural library: const stem = natural.PorterStemmer.stem(word).
Or Python with NLTK: from nltk import FreqDist; fdist = FreqDist(words).
Iterate over all documents in a corpus, aggregate frequencies, output to CSV.
Use this tool for one-off ad hoc analysis without coding.

SEO tools (Surfer, Clearscope, Frase)

These tools automate competitor comparison and term suggestions.
They include built-in TF-IDF analysis and content briefs.
Use them for paid, automated content optimization at scale.
Use this tool for free, manual analysis when you need quick frequency counts without a subscription.

Word Frequency Counter vs. alternatives

Side-by-side with common word analysis tools.

Capability	Grigora	WordCounter.net	TextFixer	SurferSEO	Spreadsheet
Word frequency table	Yes	Yes	Yes	Yes	Yes
Stop word filter	Yes	Yes	No	Yes	No
Bigram and trigram analysis	Coming soon	Paid	No	Yes	No
Stemming (run/runs/running unified)	Coming soon	Paid	No	Yes	No
Keyword density percentage	Yes	Yes	No	Yes	No
Free with no signup	Yes	Yes	Yes	Trial only	Yes
Visual word cloud	Coming soon	Yes	No	Yes	No
CSV export	Coming soon	Paid	No	Paid	No

Common errors and fixes

Eight issues users hit.

Stop words dominate the frequency results

Cause: Stop word filter is disabled, so "the", "and", "of", "is" appear at the top of the list.

Fix: Enable the stop word filter toggle. The list excludes 50-150 common English function words.

Plural and singular counted separately

Cause: No stemming applied; "shoe" and "shoes" appear as separate entries.

Fix: Use a tool with optional stemming (Porter or Snowball algorithm) to normalize word forms.

Analysis is case-sensitive (CAPITAL different from lowercase)

Cause: Default analysis treats "SEO" and "seo" as different words.

Fix: Lowercase all text before analysis. This tool does this by default.

Hyphenated words split incorrectly

Cause: "State-of-the-art" tokenizes as "state", "of", "the", "art" instead of one phrase.

Fix: Use a tokenizer that preserves hyphens, or analyze bigrams/trigrams to surface multi-word phrases.

HTML tags counted as words

Cause: Pasting raw HTML source includes tag names ("div", "span") in word frequency.

Fix: Paste only rendered text. This tool strips HTML before tokenizing.

Numbers and dates skew results

Cause: Years (2026), prices ($99), and IDs (12345) appear as high-frequency words in product/news content.

Fix: Use the optional number-stripping filter to exclude numeric tokens from analysis.

Non-English text not handled correctly

Cause: Tokenizer assumes whitespace-separated words; languages like Mandarin require character-level segmentation.

Fix: Use a language-specific tokenizer (jieba for Mandarin, MeCab for Japanese). This tool supports English and Latin-script languages.

Compound words counted as components

Cause: "Smartphone" is one word but tokenizer might split if it sees punctuation; some compound words are inconsistently spaced.

Fix: Most modern tokenizers handle compounds correctly. For German compounds, use a specialized German tokenizer.

Original data

2026 study.

1.3%

Average top-ranking primary keyword density

Recommended max density before stuffing risk

150-180

Standard English stop word list size

0.8-2.5%

Optimal density target for SEO posts

Frequently asked questions

Twelve answers.

Related free tools

Other utilities.

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