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Stopwords Remover
Strip out common filler words — the, is, and, of, to — to leave the meaningful words behind, handy for keyword extraction and text analysis. Runs in your browser.
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Quick Answer
Paste text and the tool removes common stop words — the high-frequency, low-meaning words like the, is, and, of and to — leaving the words that carry the content. It uses a built-in list of around fifty common English words and matches them case-insensitively. It is useful for keyword extraction and prepping text for analysis, though it is English-only and the short list is not exhaustive. It runs entirely in your browser.
What the Stopwords Remover Does
Enter your text and press the button to get it back with the filler words taken out. What remains is the set of meaningful words — the nouns, verbs and adjectives that actually distinguish your content — which is the form you often want before counting keywords or analysing a piece of writing.
Stop words are the most common words in a language, the ones that appear in almost every sentence and carry little distinguishing meaning. Removing them shrinks the text to its signal and is a standard first step in natural-language processing.
How It Works
The tool splits your text into words and drops any whose lowercase, letters-only form is on its built-in stop-word list — about fifty of the most common English words, including articles, prepositions, conjunctions and pronouns. Everything not on the list is kept, joined back together with spaces.
Matching is case-insensitive and ignores punctuation for the comparison, so The, the and the all count as the stop word the and are removed. The result is a terser version of your text containing mainly content words.
Generation algorithm
- Split into words. Break the text into individual words.
- Normalise each word. Lowercase it and strip punctuation for the comparison only.
- Drop the stop words. Remove any word whose normalised form is on the built-in list.
- Rejoin the rest. Join the remaining content words back into text.
Stop words it removes (sample)
articles — a, an, the
conjunctions — and, or, but
prepositions — of, to, in, on, at, for, by, from
be-verbs — is, are, was, were, be, been
pronouns — i, you, he, she, we, they, itAbout fifty common English words in all; anything not on the list is kept.
Technical Details
| Removes | common English stop words |
|---|---|
| List size | about fifty words |
| Matching | case-insensitive, punctuation ignored |
| Keeps | all words not on the list |
| Language | English only |
| Where it runs | In your browser — nothing uploaded |
Standards & references
- Stop words — high-frequency, low-meaning words — articles, prepositions, conjunctions, common pronouns and be-verbs — that natural-language processing often removes to focus on content words.
- No universal list — there is no single official stop-word list; this tool uses about fifty common English words, while larger lists (such as the widely used NLTK list of 179) remove more.
- Task-dependent — removing stop words helps keyword work, but it can change meaning — a phrase like 'not good' loses its sense if a negation is stripped — so it is not right for every task.
Accuracy & Limitations
The built-in list is short — about fifty words — so it removes the most common fillers but not every stop word a larger list would. Bigger lists, like the widely used NLTK list of 179 words, strip more; if you need a heavier clean, the result here will still contain some common words.
It is English-only. The list is English words, so it does little for text in other languages, which have their own stop words.
Removing stop words changes meaning and readability. The output reads as terse, telegraphic text — fine for keyword extraction, but not something to publish. And in tasks like sentiment analysis, stripping words can flip meaning, so use it where losing the small words is acceptable.
It matches whole words by their letters-only form. A stop word stuck to punctuation is still removed with its punctuation, and a content word is kept exactly as written, including its punctuation.
It does not stem or lemmatise. It only removes listed words; it does not reduce running to run or merge word forms, which a fuller text-processing pipeline would.
Real-World Use Cases
Keyword extraction
Reduce text to content words before counting keywords.
Text analysis prep
Clean text as a first step for word-frequency or topic work.
Tag and summary ideas
See the meaningful words to draw tags or a gist from.
Tightening notes
Strip filler to skim the substance of a passage.
When to use it — and when not to
Good for
- Keyword extraction and text analysis
- Reducing text to its content words
- A quick first-pass clean of English text
- Seeing the meaningful words in a passage
Not the best choice for
- Text you intend to publish or read normally
- Languages other than English
- Tasks where small words change meaning (sentiment)
- A heavy, exhaustive stop-word clean
Need an exhaustive clean, stemming or other languages? Use a natural-language toolkit (such as NLTK or spaCy) with a fuller stop-word list. Just counting words? A word-frequency tool can ignore common words as an option.
Frequently Asked Questions
What are stop words?
The most common words in a language — the, is, and, of, to and similar — that appear in nearly every sentence and carry little distinguishing meaning. Removing them leaves the content words that matter for analysis.
How many words does it remove?
It uses a built-in list of about fifty common English words — articles, prepositions, conjunctions, common pronouns and be-verbs. Anything not on the list is kept.
Is the list complete?
No. It is a short, practical list. Larger lists, like the widely used one of 179 words, remove more, so the output here may still contain some common words.
Does it work for other languages?
No. The list is English, so it does little for text in other languages, which have their own stop words and would need their own lists.
Can removing stop words change the meaning?
Yes. The result is terse and can read oddly, and in some tasks — sentiment analysis, for instance — stripping a small word like a negation flips the meaning. Use it where losing the filler is acceptable, such as keyword work.
Is it case-sensitive?
No. It matches case-insensitively, so The, the and the are all treated as the same stop word and removed.
What happens to punctuation?
Punctuation is ignored when checking whether a word is a stop word, so a stop word is removed along with any punctuation attached to it, while content words keep their punctuation.
Does it reduce words to their root, like running to run?
No. It only removes listed stop words; it does not stem or lemmatise. For that you would use a fuller natural-language pipeline.
Why would I remove stop words?
To focus on the words that distinguish your content — useful for keyword extraction, word-frequency analysis and topic modelling, and it reduces the amount of text a process has to handle.
Will the output read normally?
No. It is meant for analysis, not reading, so it comes out terse and telegraphic. Keep your original for anything you plan to publish.
Can I add my own stop words?
Not here — the list is fixed. For a custom list, a text-processing library lets you define exactly which words to remove.
Is my text uploaded?
No. The removal happens entirely in your browser; nothing is sent anywhere.
References
Removes about fifty common English stop words, matching case-insensitively and ignoring punctuation; it is explicit that the list is short (larger lists remove more), English-only, and that stripping stop words is for analysis (it changes meaning and readability, so not for publishing).
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