Paste any text to see which words come up most often. This word frequency counter ranks every word by how many times it appears and shows the totals — useful for checking repeated words in your writing, finding keywords, or studying a piece of text. It all runs in your browser.
About this word frequency counter
A word frequency counter tells you how often each word appears in a piece of text and ranks them from most to least common. Writers use it to catch words they lean on too much, students and researchers use it to study a text, and anyone working on SEO can use it to check which terms a page emphasises. By default it treats The and the as the same word, but you can switch that off. The most common words are listed with their counts, and the totals show how many unique and total words your text contains. Everything is worked out in your browser.
Frequently asked questions
- What counts as a word?
- Any run of letters or numbers. Punctuation and spaces are treated as separators, so "don't" stays as one word.
- Does capitalisation matter?
- By default no — "Apple" and "apple" are counted together. Tick the box to count them separately.
- Is there a limit on text length?
- There is no fixed limit. The tool runs in your browser, so very large texts depend only on your device's memory.
Reading a word frequency list
Word frequency analysis relies on tokenization, which is the process of breaking a continuous stream of text into individual units. The tool scans the input string and identifies boundaries defined by whitespace and punctuation marks. Each identified token is normalized to a base form, typically by removing case sensitivity, before being added to a hash map data structure. This structure tracks the unique identifier of the word as the key and increments an associated integer value every time that key is encountered.
The core logic follows Zipf’s Law, an empirical observation stating that the frequency of any word is inversely proportional to its rank in the frequency table. In a large corpus of natural language, the most frequent word will appear approximately twice as often as the second most frequent word, three times as often as the third, and so on. While this mathematical distribution is predictable across vast datasets, small text samples often deviate from this curve due to the limited scope of the vocabulary used.
The most common mistake users make involves failing to account for stop words, which are high-frequency function words like the, is, and at. These words frequently occupy the top spots in any analysis, often masking the thematic content of the text. To gain meaningful insight, researchers often filter these common terms out to focus on content-heavy nouns and verbs. Ignoring the impact of these ubiquitous words leads to skewed results that prioritize grammatical structure over the actual subject matter.