Stop Words Remover

How to use

1. 1Paste your text into the input box on the left. Word and character counts update as you type.
2. 2Pick a stop word list: Minimal for a conservative strip, Extended for an aggressive SEO-style clean, or Empty to build a custom list from scratch.
3. 3Add brand names, boilerplate, or domain filler in the Also remove these words box. Use commas, spaces, or new lines as separators.
4. 4Add negations or qualifiers like not, no, never, very to the Never remove these words box so they survive the cleanup.
5. 5Adjust the toggles: turn on case-sensitive matching if you only want exact case matches, keep contractions matching on by default, and leave whitespace collapsing on so the output does not have double spaces.
6. 6Read the output text on the right, copy it with the Copy output button, or replace the input with the cleaned version to chain another pass.
7. 7Review the removed word chips and the removal rate. If too much was removed, switch to the minimal list or add the over-removed words to the Never remove list.

About this tool

Stop Words Remover strips common English function words from a piece of text and gives you back the content words. Paste an article, a blog post draft, a product description, a transcript, a research note, or any block of prose, and the tool tokenizes the input in your browser, walks every word run, and either keeps or removes it based on the active stop word list. Three presets cover the common cases: a minimal NLTK-style list of about 150 articles, pronouns, common prepositions, and basic forms of the verb to be; an extended SEO and blog list that adds modals, filler verbs, contracted forms, and connectors for a more aggressive strip; and an empty preset for users who want to define their own list from scratch. Two custom textareas let you fine-tune the result: add brand names, boilerplate, or other domain-specific filler to remove, and pin words like not, no, never, and very to the keep list so negations and qualifiers that change meaning survive. A case-sensitive toggle controls whether The and the are treated as the same word, a contraction toggle decides whether don't matches a dont entry, and a whitespace toggle collapses the extra spaces left around a removed word without flattening paragraph breaks. Tokenization is Unicode-aware via the \p{L} and \p{N} character classes so accented Latin, Cyrillic, Greek, and similar scripts tokenize cleanly, and every punctuation mark, line break, and paragraph break in the input is preserved verbatim in the output. The result panel shows the cleaned text with a copy button, plus removal statistics: input word count, output word count, stop words removed, removal rate as a percentage, and a chip cloud of the first sixty removed words so you can audit which terms the list caught. Useful for SEO content prep, keyword and tag extraction, search index pre-processing, summarization warm-up, vocabulary lists, AI prompt cleanup, NLP homework, and any workflow where you want to focus on what a piece of text is actually about. Everything runs locally on your device, so the drafts and notes you paste here never leave your browser.

Free to use. Works in your browser. No signup, no login.