Word Frequency Counter

What this tool does

Word Frequency Counter ranks the words and phrases in your text by how often they occur. Filter out common function words and your own list, set minimum lengths and counts, sort the table, and export it as CSV for spreadsheets or further analysis.

Knowing which words dominate a text answers many questions: whether an essay leans on the same adjective, which topics customers mention most in survey comments, or which phrases a competitor’s landing page repeats. Counting by hand is impossible beyond a paragraph, and a word processor will only find one word at a time.

This counter splits text with the browser’s Unicode word segmentation, so punctuation, apostrophes and non-Latin scripts are handled properly, then tallies every term. Besides single words it can count n-grams, consecutive two- or three-word phrases such as “customer service team”, which often reveal more than single words. Around 120 common English function words can be ignored, and phrases are only dropped when they begin or end with one, so “cost of living” survives. Analysis runs locally in your browser.

How to use Word Frequency Counter

1. Paste your text, drop a .txt file onto the editor, or click Sample.
2. Choose what to Count: single words, two-word phrases or three-word phrases.
3. Decide whether to “Ignore common words”, whether counting is case sensitive and whether numbers are included.
4. Set “Minimum word length” and “Minimum occurrences” to remove noise, and add any extra words to ignore.
5. Pick a “Sort by” order and how many rows to show.
6. Copy the table as tab-separated text or download it as CSV.

Tips for better results

• Two-word phrases are the quickest way to find repeated clichés such as “in order” or “very important”.
• Add your brand or product name to the ignore list to see which other terms stand out.
• Set minimum occurrences to 3 on long documents to focus the table on genuinely recurring terms.
• The Share column is the term’s count divided by all counted terms after filtering, not by total words.

Common use cases

• Finding the most frequent themes in open-ended survey answers or product reviews.
• Spotting overused words while editing a novel chapter or a thesis.
• Comparing keyword focus between two versions of a web page.
• Building vocabulary lists from a foreign-language article for study.

Common mistakes to avoid

• Reading raw frequency as importance; a word that appears often may simply be a structural term like “section”.
• Leaving case sensitivity on, which splits “Coffee” and “coffee” into separate rows.
• Analysing a very short text, where a single repetition changes the ranking dramatically.