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About this tool
Understanding which words appear most often in a text reveals patterns that are invisible during normal reading — the overused filler words in your writing, the dominant topics in an article, the keyword density of a blog post for SEO analysis, or the most common terms in a corpus of documents. Word Frequency Counter analyzes any text and produces a ranked list of every unique word paired with its occurrence count. Configurable options let you ignore case (so 'The' and 'the' are counted together), filter out common stop words like 'a', 'the', and 'is', and set a minimum word length to exclude short noise words. The results can be exported as plain text or CSV for further analysis in a spreadsheet or data tool.
Why use it
Ranked Word Frequency Table: Every unique word listed with its count, sorted from most to least frequent — see your top words at a glance.
Stop Word Filtering: Remove common English function words so only content-carrying words appear in the frequency list.
Case-Insensitive Counting: Merge 'Apple', 'apple', and 'APPLE' into a single entry so capitalization does not inflate counts.
Minimum Word Length Filter: Set a threshold to exclude very short words that are typically noise rather than meaningful terms.
CSV Copy for Spreadsheet Analysis: Copy the ranked list as CSV (word,count) or tab-separated text and paste it into Excel, Google Sheets, or a data tool.
SEO Keyword Density Analysis: Identify your primary and secondary keywords and verify they appear with appropriate frequency in your content.
How to use
- Paste your text into the Input text box.
- The frequency table populates instantly, sorted by count descending.
- Tick 'Case sensitive' if 'The' and 'the' should be counted separately (off by default).
- Leave 'Ignore common stopwords' ticked to filter out filler words like 'a', 'the', 'is'.
- Set Min word length to exclude short words, and Top results to limit how many rows are shown.
- Click Copy as CSV or Copy as text to copy the ranked list for a spreadsheet or document.
When it helps
- When analyzing keyword density in a blog post before publishing to verify SEO targeting.
- When identifying overused words in your writing that should be varied for better readability.
- When studying a text corpus to find the most dominant topics or terms.
- When preparing a tag cloud or word cloud visualization and needing the frequency data.
- When checking whether specific keywords appear enough times (or too many times) in an article.
- When comparing word frequencies between two different texts to analyze style differences.
Examples
Inputred blue red green red blue
Expected resultWith case-insensitive counting and stopwords off: red: 3, blue: 2, green: 1
InputThe cat sat on the mat
Expected resultWith stopwords on: cat: 1, mat: 1, sat: 1
Tips
- Enable stop word removal and set minimum length to 4 for the cleanest keyword frequency view in blog post analysis.
- Compare the top 10 words in your draft against your target SEO keywords to verify alignment before publishing.
- Export to CSV and create a word cloud in Google Sheets or a visualization tool using the word and count columns.
- Run the same article through the frequency counter before and after editing to see whether revision reduced overused words.
- For topic modeling, look at the top 20 words after stop word removal — they form a reliable summary of the text's main themes.
Frequently Asked Questions
What counts as a word for frequency analysis?⌄
A word is a run of the English letters A–Z and digits, optionally joined by apostrophes, so "don't" stays one word. Punctuation is ignored, so 'word,' and 'word.' both count as 'word'. Hyphens split words: 'well-known' counts as 'well' and 'known'.
What stop words does the tool filter?⌄
The stop word list includes common English function words: articles (a, an, the), prepositions (in, on, at, by, for, with, of), conjunctions (and, but, or, so), auxiliary verbs (is, are, was, were, be, been, have, has, had), and pronouns (I, you, he, she, it, we, they).
Does it work on non-English text?⌄
Only partly. Words are matched using the basic Latin letters A–Z, so accented letters break words apart ('café' counts as 'caf') and scripts such as Cyrillic, Greek, Arabic or Chinese are not counted at all. The stop word list is English too.
What is a good keyword density for SEO?⌄
Most SEO recommendations suggest 0.5%–2.5% density for your primary keyword. If your article is 1,000 words, your target keyword appearing 10–25 times is in a healthy range. Over-repetition is flagged as keyword stuffing.
Can I export the results?⌄
Yes, through the clipboard. Copy as CSV copies a two-column list with a word,count header, and Copy as text copies one 'word<tab>count' line per row. Both include only the rows currently shown by Top results. Paste into Excel or Google Sheets for further analysis.
Does it count partial word matches?⌄
No. The tool matches whole words only. 'run' and 'running' are counted separately, not combined. For stemmed counting, you would need a dedicated NLP tool.
Can I use this to analyze multiple documents?⌄
Paste all your text into a single input — concatenate multiple documents with a separator if needed. The frequency count will reflect the combined corpus.
Is there a limit on input text size?⌄
There is no enforced limit. Very large inputs (millions of words) may cause a brief processing delay, but typical article or document sizes process instantly.
Glossary
- Word frequency
- The number of times a specific word appears in a text. Used in linguistics, SEO, and text analysis to understand content emphasis.
- Stop words
- Common function words (the, a, is, in, of) that carry little semantic content and are typically excluded from frequency analysis and search indexing.
- Keyword density
- The percentage of words in a text that are a specific keyword. Calculated as (keyword occurrences / total words) × 100.
- Tokenization
- The process of splitting text into individual tokens (words or subwords) for analysis. Simple tokenization splits on whitespace and strips punctuation.
- Corpus
- A collection of texts used as a dataset for linguistic analysis, machine learning, or frequency counting.
- Word cloud
- A visual representation of word frequencies where more frequent words appear in larger font sizes. Also called a tag cloud.
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