PRPickrack

Word Frequency Counter

Count word/character/line occurrences, ranked top N. Filter by length, drop stopwords. Export CSV.

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Paste text to see frequencies

Count word, character, or line occurrences in any text. Ranked top N, filter by length, drop stopwords. Export CSV.

Word Frequency Counter scans any text and counts how often each word (or character, or line) appears. Sorts results by frequency, shows the top N, and lets you filter by minimum length and drop common English stopwords (the, a, of, ...). Export to CSV for further analysis in Excel or a notebook.

Use it for: SEO keyword density analysis on your own content, transcript analysis (what topics did the speaker cover most?), literary analysis (Hemingway's vocabulary diversity), customer feedback themes, log error frequency, or any text-mining task that needs occurrence counts.

Free, no signup, browser-side. Tokenizer handles English, Vietnamese, Chinese, Japanese, and most Latin/Cyrillic scripts. Up to ~1MB of input processed instantly.

Key features

  • 3 count modesWords (default), characters (no whitespace), or lines. Switch modes instantly without re-pasting.
  • Top N rankingConfigurable from 5 to 500. Shows count + percentage + visual bar relative to the #1 word.
  • Min length filterSkip 1-2 letter noise. Set min length 3 or 4 for cleaner SEO analysis.
  • English stopwords175+ common stopwords (the, a, an, of, and, ...) — drop them to surface meaningful content words.
  • Case-insensitive defaultCat, cat, CAT count together. Toggle for case-sensitive analysis.
  • CSV exportDownload or copy as CSV: rank, word, count, percentage. Open in Excel / Numbers / Sheets directly.
  • Unicode-aware tokenizerHandles Vietnamese diacritics, Chinese, Japanese, Korean, Cyrillic, and Latin scripts correctly.

How to use

  1. Step 1: Paste textAny prose — article, essay, transcript, comments, logs, novel. Multiple paragraphs OK.
  2. Step 2: Pick count modeWords is most common. Characters reveals frequency of letters (cryptanalysis). Lines counts unique lines (log analysis).
  3. Step 3: Tune filtersMin length 3 cuts pronoun noise. Stopwords ON drops fillers. Top N caps the result list.
  4. Step 4: Read or exportLive ranking on the right. Click CSV to copy/download for spreadsheet analysis.

When to use

  • SEO keyword density — check your blog post's top 20 words match your target keyword
  • Transcript analysis — pull dominant topics from a 90-min meeting transcript
  • Literary analysis — Hemingway uses 1,500 unique words in 80,000 — compare to Tolstoy
  • Customer feedback theming — paste all support ticket subjects → see complaint clusters
  • Log error pattern detection — most-common error message across 10k log lines
  • Email subject A/B testing — frequency of high-CTR vs low-CTR subject words
  • Vocabulary diversity score — unique words / total words ratio (TTR) for your writing

Frequently asked questions

What counts as a 'word'?

A run of letters / digits / apostrophes / hyphens separated by whitespace or punctuation. Words are lowercased (in case-insensitive mode), then matched exactly. So 'don't' is one word, 'state-of-the-art' is one word, but 'don't!' is parsed as 'don't' (the ! is stripped).

What languages are supported?

All Latin scripts (English, French, German, Vietnamese with diacritics, Spanish, ...), Cyrillic, Chinese (each character is a word), Japanese (mixed kanji/kana counted per token), Korean. The tokenizer is Unicode-aware via regex.

Why are some short words missing?

Either the Min Length filter is dropping them (try 1 or 2), or they're in the English stopwords list (toggle off). Both are common defaults — turn off if you want raw counts.

What's the stopwords list?

~175 English words — articles (the, a), prepositions (of, in, on), pronouns (he, she, it), auxiliary verbs (is, are, was), and very-common conjunctions (and, but). Standard NLTK English stopwords. Non-English stopword lists are on the roadmap.

Can I count bi-grams or n-grams?

Not in v1 — single words only. For n-gram analysis use a Python NLTK script or cmder ngrams.py. n-gram support is on the roadmap.

How is this different from a regular word counter?

Word counter (also a Pickrack tool) tells you TOTAL count of words. Word Frequency tells you how often EACH word appears. Different jobs.

How big can my input be?

Tested to 1MB of text (~150,000 English words). Above that the tokenizer slows but works. For book-length analysis (10MB+) use a Python script.

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