What Word Frequency helps you do

Count distinct words and list the most common tokens with their totals. It gives a quick picture of what a block of text is actually made of, which helps with editing, keyword review and data cleanup. Words are compared case-insensitively, so The and the count as one token.

Frequency analysis runs locally in your browser; no text is uploaded to Haivix.

How it works

These tools scan the pasted text in the browser with a pattern or parser and return the matches in first-seen order. Extraction is a candidate list, not a verification step: an email-shaped string may not be deliverable, a URL may not resolve, and a number may be part of a date or an identifier. Nothing is uploaded, and the source text is never changed. The result is a snapshot of the pasted text; editing the source afterwards does not update the list.

How to use Word Frequency

Paste the text you want to analyze.
Set the number of top words to show.
Run the count and scan the most frequent terms.
Copy the list for further analysis.

When to use it

Find the most repeated terms in an article draft.
Spot filler words in a document before editing.
Check keyword frequency in a piece of copy.
Compare the vocabulary of two drafts.
Find repeated terms in a set of support tickets.
Check whether a page overuses a brand term.
Find repeated phrases in a set of support tickets.

When not to use it

Do not use it as a readability or grammar check.
Do not expect stemming; run and running are separate.
Do not treat the top word as the most important concept.

Example

A short paragraph that uses the word the six times lists the with a count of six at the top. If two words share a count, the alphabetical order decides between them. The unique total counts every distinct token, not just the listed top words.

What you can do with Word Frequency

Count each distinct word case-insensitively.
Sort by count, then alphabetically for ties.
Limit the list to the top N words.
Show the number of unique words and listed items.
Copy the frequency list.
Run the analysis locally with no upload.

Example: check the matches on a sample

Paste a short sample first and count the expected matches by eye. Compare that count with the result before running a large file, because a missing match is easier to spot in a small sample.

Read a few matches in context to catch false positives. A phone-like number inside an order ID, or a URL inside a code snippet, is common and should not be treated as a contact or a live link.

Copy the list and keep the original. If the extraction feeds a mail merge or a report, verify a sample of the destinations rather than assuming every match is valid.

If the result is empty, check the input format before assuming the tool is broken. A bare domain without http, an address split across a line, or JSON with a trailing comma are common reasons for no matches.

For a list that will be contacted or published, keep the source and the extracted list side by side. The source is the only way to resolve a questionable match or explain why one was missed.

What to know before you start

Word boundaries are language-dependent, and punctuation handling affects the counts.
It does not perform stemming, so related forms such as run and running are counted separately.
There is no stop-word filter; common words dominate.
It counts tokens, not phrases or lemmas.

Common mistakes

Assuming the most frequent word is the most meaningful.
Expecting plural and singular forms to be grouped.
Forgetting that common words like the dominate the list.
Treating the count as a keyword-density score.
Assuming the list includes every unique word; it shows the top N.

Troubleshooting

If a word is missing, it may be below the top-N limit or split by punctuation.

If related forms appear separately, the tool does not stem.

If the counts look low, check for a very short text or a high punctuation density.

If the list is empty, the text may contain no words.

Word Frequency