// count and rank every word in seconds
Count and rank every unique word in any text block. Get frequency counts, percentage breakdown, and sortable word lists instantly in your browser.
Ready to analyze
Paste text and click Analyze to see word frequenciesCopy any text — articles, emails, essays, transcripts, code comments — and paste it in the input area.
Choose whether to filter stopwords, use case-insensitive matching, and whether to include numeric tokens.
Click Analyze to get ranked results. Filter the list, change sort order, or export the full frequency table as CSV.
The Word Frequency Analyzer scans any block of text and ranks every unique word by how often it appears. You get raw counts, percentages relative to total word count, and visual frequency bars — all computed instantly in your browser with no data sent to any server.
Stopwords are extremely common words like "the", "a", "is", "in", "and" that appear frequently in almost all English text but carry little semantic meaning. Filtering them lets you focus on the meaningful content words in your text.
No — all analysis runs entirely in your browser using JavaScript. Your text never leaves your device. This makes the tool fast, private, and safe to use with sensitive content.
Each word's percentage is its count divided by the total number of words in the analyzed text (after applying your filters), then multiplied by 100. It shows how much of the text is made up of that specific word.
Yes — the frequency counting works on any language. However, the built-in stopword filter only covers English stopwords. For other languages, disable the stopword filter to get unfiltered results for all words.
The CSV export includes all columns: rank, word, count, and percentage. You can open it in Excel, Google Sheets, or any spreadsheet tool for further analysis, charting, or reporting.
The word cloud displays the top 30 most frequent words (after filtering) with font size scaled proportionally to frequency. The most common words appear larger, giving you a visual overview of your text's key terms at a glance.
A word frequency analyzer is a text analysis tool that reads a block of text and counts how many times each unique word appears. The results are ranked by frequency, giving you an instant picture of which words dominate your writing. This type of analysis has applications across SEO, academic research, creative writing, data science, and linguistics.
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Search engines pay close attention to the words that appear most frequently in your content. A word frequency analysis helps you verify that your target keywords appear with appropriate density — not so little that they're invisible to crawlers, and not so often that your content reads as keyword stuffing. Running your draft through a frequency analyzer before publishing is a fast sanity check for on-page SEO.
Beyond raw keywords, frequency analysis can reveal semantic clusters — groups of related words that reinforce your topic expertise. Pages that naturally use a rich vocabulary around a topic tend to rank better because they signal comprehensive coverage to modern search algorithms.
Writers often develop habitual word choices without realizing it. Frequency analysis can expose overuse of certain adjectives, transition words, or filler phrases. If "very", "really", or "just" show up in your top-ten list, that's a clear signal to revisit your prose. Professional editors have long used frequency tools as part of manuscript review, and now the same power is available to anyone in seconds.
Academic writing benefits particularly from this kind of analysis. Papers with a focused vocabulary tend to score higher on clarity and coherence metrics. Running your thesis or research paper through a frequency counter can also help you spot inconsistent terminology — for example, if you alternate between "methodology" and "method" without clear reason.
Stopwords are the most common words in a language — articles, prepositions, conjunctions, and auxiliary verbs like "the", "of", "and", "to", "a", "in", "is", "it", "you", "that". In virtually any English text, these words will dominate a raw frequency count without providing useful information about the content.
Our built-in stopword list covers over 120 common English function words. When the stopword filter is enabled, these tokens are excluded from the frequency table so your results show only meaningful content words. You can toggle this filter off if you need the complete picture — for example, when analyzing writing style rather than content topics.
The percentage column shows each word's share of the total word count in the analyzed text. For example, if your text has 500 words and the word "data" appears 10 times, its frequency percentage is 2.00%. This normalized view is essential when comparing texts of different lengths — absolute counts don't tell you much, but percentages let you compare word density across articles, documents, or authors meaningfully.
The CSV export includes rank, word, count, and percentage for every word in your results. This makes it easy to continue analysis in a spreadsheet. You can build pivot tables, create word frequency charts, merge results from multiple texts for comparison, or feed the data into other analysis tools. The export respects your current filter settings — what you see in the table is what you get in the file.
Word frequency distribution is a foundational concept in natural language processing. Zipf's Law — the observation that word frequency is roughly inversely proportional to frequency rank — was discovered through exactly this kind of counting. The most common word in any large corpus appears approximately twice as often as the second most common, three times more than the third, and so on. You can observe this distribution directly in the results table when analyzing longer texts.
Frequency tables are also the starting point for building bag-of-words models, TF-IDF scores, and simple text classifiers. If you're learning NLP, generating a frequency table by hand (or with a simple tool like this one) before moving to libraries like NLTK or spaCy is an excellent way to build intuition about what machines see when they process language.