Word Frequency Counter
Paste any text to see a ranked table of word frequencies — useful for SEO analysis and writing improvement.
How to use Word Frequency Counter
The Word Frequency Counter analyses a body of text and produces a ranked frequency table showing how often each word appears. It supports stop-word filtering (removing common words like "the", "and", "is"), case normalisation and minimum frequency thresholds — making it a powerful tool for keyword research, plagiarism detection, text summarisation and building word clouds.
- Paste or upload the text you want to analyse into the input area.
- Toggle "Ignore stop words" to filter out common function words that dominate most texts.
- Set the minimum frequency threshold to focus on words that appear at least N times.
- Choose whether to treat the analysis as case-sensitive (default: case-insensitive).
- The ranked frequency table updates instantly; click any column header to sort by word or count.
- Export the frequency table as CSV for use in spreadsheet tools or word-cloud generators.
Your data never leaves your device — 100% private processing.
Stop words and why they matter
Stop words are the most common words in a language — articles (a, an, the), conjunctions (and, but, or), prepositions (in, on, at) and auxiliary verbs (is, are, was). They typically account for 40–50% of all word occurrences in English prose but carry almost no semantic meaning on their own. Filtering stop words reveals the content-bearing vocabulary of a text. Different NLP tasks use different stop-word lists: search engines filter stop words from queries; text classifiers may preserve them because their presence or absence can be a style signal.
Applications: keyword research and text analysis
Word frequency analysis is a foundational technique in computational linguistics and content strategy. For SEO keyword research, comparing word frequencies across top-ranking pages reveals which terms an article must cover to be semantically relevant. For academic research, frequency tables identify the most cited concepts in a corpus. In UX, frequency analysis of customer support tickets surfaces the most common problems. The Zipf's law observation — that the most frequent word appears roughly twice as often as the second, three times as often as the third, and so on — holds surprisingly well across natural languages and text genres.
| Use case | Target keyword density | Notes |
|---|---|---|
| SEO blog post (primary keyword) | 0.5%–1.5% | Higher density risks keyword stuffing penalty |
| Product description | 1%–2% | Focus on benefit language naturally |
| Academic paper | No strict target | Use synonym variation; avoid repetition |
| PPC ad copy | 2%–4% | Match keyword to ad for Quality Score |
| Meta description | 1 instance | 160 char limit; exact match improves CTR |
| General web copy | 0.5%–1% | Write naturally; use synonyms and related terms |
Glossary
- Stop word
- A common function word (the, and, is) filtered out before frequency analysis because it adds little semantic value.
- Zipf's law
- The empirical observation that in natural language, word frequency is inversely proportional to frequency rank.
- Corpus
- A large, structured collection of text used for linguistic analysis or training language models.
- Lemmatisation
- Reducing inflected word forms to their base (lemma) so "running", "ran" and "runs" all count as "run".
- TF-IDF
- Term Frequency–Inverse Document Frequency — a weight that balances how often a word appears in a document vs how common it is across all documents.
Related reading
Frequently Asked Questions
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Why use Word Frequency Counter?
- Instant results with no signup or account creation
- Works offline once the page is loaded
- Supports Unicode and multilingual text
- Copy results to clipboard with a single click
Common use cases
- Count words in an essay before submission
- Sort a list of items alphabetically
- Remove duplicate lines from CSV exports
- Change the case of text copied from PDFs
- Find and replace text across large documents
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