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Clean text • Remove filler words fast

Stop Words Remover

Paste your text and remove common English stop words — or keep only them for quick filler analysis. Copy the output or download it as a TXT file.

Input text
Tip: Stop words are common function words (the, and, of). Removing them is useful for keyword-focused analysis — not for publishing.

Output

Cleaned text
Below: top stop words (removed in “remove” mode / kept in “keep only” mode).
Stop word Count
Run the tool to see the table.

Quick interpretation

Stop-word filtering helps with keyword extraction and quick content analysis.

  • Remove keeps content-heavy words
  • Keep only highlights filler density
  • Don’t publish “cleaned” text as-is
Text cleanup

Stop Words Remover: clean text for keyword-focused analysis

Stop words are extremely common words that carry little standalone meaning (for example: the, and, of). Removing them can make it easier to spot the main terms in a paragraph, compare drafts, and build quick keyword lists.

How to use this tool

  • Paste text into the input field.
  • Choose a mode: remove stop words or keep only stop words.
  • Adjust options to keep numbers, apostrophes, and hyphens (useful for tokens like it’s, T4, 3.7V).
  • Copy the output or download it as a TXT file.

Common use cases

  • Keyword extraction: quickly see what the text is really about.
  • Draft comparison: remove noise before comparing two versions.
  • Filler detection: “keep only stop words” mode highlights function-word density.

Important note

This is an analysis tool. Removing stop words from real articles can hurt readability and meaning. Use the cleaned output for research, clustering, and internal analysis — not for publishing.

FAQ

What are stop words, and why do SEO tools remove them?

Stop words are very common function words such as the, and, of, to. They appear in almost every sentence and usually don’t help identify a topic.

Removing them is useful for analysis (keyword extraction, clustering, frequency checks) because it reduces noise and makes content-heavy terms stand out.

Is removing stop words a good idea for published SEO content?

No. Stop-word removal is for analysis, not for rewriting articles for publishing. Removing them from real content can break grammar, reduce clarity, and change meaning, which increases bounce and hurts user signals.

Use the cleaned output for internal checks (term lists, comparisons), not as final copy.

What does “keep only stop words” mode show?

It removes everything except stop words. This helps you see how much of the text is filler vs. meaningful terms and can quickly reveal “wordy” writing.

If the output is unusually long, the input may be heavy on function words and light on specific nouns/verbs.

Why does the tool show a “top stop words” table?

The table highlights which stop words dominate the text. In remove mode it shows the most frequently removed stop words; in keep only mode it shows the most frequently kept stop words.

This is useful for spotting repetitive filler patterns (e.g., too many and, of, to) when you compare drafts.

Why do my “words out” and “stop words counted” numbers not match?

“Words out” is the word count in the final output after applying all settings (mode, keep numbers, token rules). “Stop words counted” is the total occurrences of stop words that were removed (remove mode) or kept (keep only mode).

They measure different things: one is the output size, the other is the stop-word frequency tracked for the table.

How does “lowercase compare” affect results?

With lowercase comparison enabled, The, THE, and the are treated as the same stop word. This is usually what you want for consistent analysis.

If you disable it, case differences can cause stop words to be missed and remain in the output.

Why would I turn off “keep apostrophes” or “keep hyphens”?

Those options control tokenization. Keeping apostrophes and hyphens preserves words like it’s, don’t, long-term as single tokens.

If you turn them off, such tokens may be split, which can change stop-word detection and frequency counts in the table.

What does “keep numbers” change, and when should I disable it?

If “keep numbers” is enabled, numeric tokens remain in the output (useful for audits, specs, versions, prices, dates). If disabled, standalone numbers are removed after stop-word processing.

Disable it when you want a cleaner term list focused on words only (for example, building a keyword set from prose).