Stop words are the glue words of a language — the, and, of, to, in — essential for grammar, invisible for meaning. When you build keyword lists, compare texts, feed an NLP pipeline or condense a draft into headline words, the glue is noise. This stop words remover strips the standard English stop-word list from any text in one paste, shows exactly what was removed and how much of the text it was, and hands you the cleaned version ready to copy.
Everything runs in your browser. ¿Texto en español? La versión española usa una lista española de verdad: Eliminador de Palabras Vacías.
Which words does it remove?
The standard English list of roughly 175 function words: articles (a, an, the), pronouns (I, you, he, she, it, we, they and their forms), prepositions (of, in, on, at, by, for, with), conjunctions (and, or, but), auxiliary verbs (am, is, are, was, were, be, been, have, has, do, does) and their common negations and contractions (isn, aren, couldn). Matching is case-insensitive and punctuation-aware: “The,” at the start of a sentence is recognised as the and removed with its comma, while the punctuation of the words you keep stays untouched. Content words — every noun, verb and adjective carrying actual meaning — are never on the list.
When removing stop words helps, and when it hurts
It helps wherever machines or analysts read text: keyword extraction and SEO briefs (a density count without the at 6% is finally readable), search-index preprocessing, word-cloud and frequency work, text classification, and condensing drafts into titles, tags and alt text. It hurts when a human has to read the result as prose: stripped text is telegraphic, not better writing. And it can destroy fixed phrases — “The Who” is a band, “to be or not to be” is a quotation, “The Hague” is a city. The removed-words list under the result exists precisely so you can audit what went out before you use the cleaned text anywhere public.
Worked example
“The quick brown fox jumps over the lazy dog, and the dog does not care at all.” Eleven-ish glue hits later, the cleaned text reads: “quick brown fox jumps lazy dog, dog care.” Removed: the ×3, over, and, does, not, at, all — about 45% of the tokens, which is typical for ordinary English prose (stop words usually make up 40–50% of running text). For a frequency analysis, that cleaned version is the dataset; for a headline, it is the raw material.
A note on the 40–50% figure
New users are often startled that nearly half their text was glue. That ratio is a property of English, not of their writing: function words are few in type but enormous in frequency — the single word the alone accounts for roughly one token in twenty in typical prose. Removing them does not make text “half as good”; it re-weights what remains so the meaning-bearing words — the ones a search index, a classifier or a headline actually uses — stand in their true proportions.
Frequently asked questions
Is the cleaned text meant to be published? No — it is a processing output for analysis, briefs and tagging. Publishing it raw would read like a telegram from 1920.
Does it change capitalisation or punctuation of kept words? No. Kept words keep their original case and punctuation; only whole stop-word tokens are removed.
Can I get the list itself? The removed words under each result show exactly which list entries fired on your text. The full list is the standard English set used by search engines and NLP libraries.
What about Spanish text? Use the Spanish version — its list is genuinely Spanish (el, la, de, en, que…), not this list translated: Eliminador de Palabras Vacías.
Related tools
Versión en español: Eliminador de Palabras Vacías: limpia tu texto de palabras de relleno gramatical · Keyword Density Checker · Word Frequency Analyzer.
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