Word Frequency Counter
Analyze how many times each word appears in your text, in your browser.
What it's for
Understand your text in depth
Works with any text
Articles, scripts, code, transcripts, emails — any textual content in any European language.
100% private
Your text is analyzed in your browser. Never sent to any server or AI model.
Quality analysis
Filter stop words, set minimum frequency, and export to CSV for advanced analysis.
Instant
The frequency ranking updates in real time as you type or paste text.
How it works
Three steps, no hassle
Paste or type your text
Enter the text to analyze. It can be an article, script, source code, transcript, or any textual content.
Configure the analysis
Choose whether to ignore stop words, set a minimum frequency threshold, and select whether the analysis is case-sensitive.
Explore the results
Words are shown sorted by descending frequency. Copy the table or download it as CSV for use in spreadsheets or analysis tools.
FAQ
Got questions?
Stop words are high-frequency words with little semantic value: articles (the, a, an), prepositions (of, in, with), conjunctions (and, or, but), pronouns (I, you, he). In any prose text, these words dominate the top positions of the frequency ranking, obscuring words with real content. By filtering them out, frequency analysis reveals thematically relevant terms: key concepts, proper names, technical terms, and main verbs.
CJK languages do not use spaces to separate words, which means whitespace-based tokenization (the standard method for European languages) does not work correctly. For Chinese and Japanese, correct segmentation requires a language-specific morphological analyzer (such as MeCab for Japanese or Jieba for Chinese). This tool applies tokenization based on spaces and punctuation, so its results are indicative for CJK texts but not linguistically precise.
Yes. You can configure a minimum frequency threshold to hide words that appear fewer than N times. This is useful for long texts where words with frequency 1 (hapax legomena) can represent 40-60% of the total vocabulary. Filtering for a minimum of 2 or 3 occurrences eliminates much noise and allows you to focus on the words that truly structure the content.
TF-IDF (Term Frequency-Inverse Document Frequency) is a metric that weights a word's frequency in a document (TF) against its frequency across a collection of documents (IDF). A word that is very frequent in a document but also very frequent in all other documents (like stop words) receives a low score. A word frequent in one document but rare in the rest receives a high score, indicating it is distinctive to that document. Simple frequency shows how many times each word appears; TF-IDF shows which words are characteristic and differentiating for that specific text.
Zipf's law, formulated by linguist George Kingsley Zipf in 1949, states that in any natural language corpus, the frequency of a word is inversely proportional to its frequency rank. The most frequent word appears twice as often as the second, three times as often as the third, and so on. This power-law distribution appears remarkably consistently across all natural languages and in many other phenomena (cities by population, companies by revenue). If your text's frequency ranking follows a Zipf curve, it is a signal that the text has natural language characteristics; marked deviations may indicate artificially generated content or highly specialized technical texts.
Word frequency: content analysis for SEO, writing, and linguistics
Word frequency analysis is one of the oldest and most versatile techniques in natural language processing. In SEO, it allows you to identify the keywords that dominate a text and compare them with target terms to verify keyword density and topical relevance. A well-optimized article has a frequency distribution where primary terms appear consistently, while excessive repetition (keyword stuffing) is penalized by search engine algorithms.
In writing and editing, frequency analysis reveals filler words and overused terms that diminish text quality. If the counter shows you used 'basically' 15 times in an 800-word article, you have concrete information to improve your style. In academic research, word frequency is the foundation of techniques like concordance analysis, term index construction, and stylistic comparison between authors (stylometry), used even for authorship attribution in anonymous texts.
Convertir.ai processes the analysis directly in your browser without sending your text to any server or exposing it to AI models. Results are available as an interactive table and as a CSV download, compatible with Excel, Google Sheets, Python (pandas), and R for more advanced analysis.