Count matches against a built-in English stop-word list. Optionally show the matched words and how often each occurs.
Paste English text, choose whether to show the matched list and select Count Stop Words. The result shows recognized tokens, stop-word occurrences and their percentage of the token total. Recalculate after editing or changing the display option.
Stop words are common words such as “the”, “and” and “of” that some text-processing workflows handle separately. A stop-word list is a chosen vocabulary for an analysis task; it is not a list of words that are wrong to use.
Input is converted to lowercase, split into recognized tokens and compared with the fixed list included in the tool. Every matching occurrence contributes to the total, even when the same word appears repeatedly.
For “The cat and the dog”, there are five tokens. “the” occurs twice and “and” once, so the stop-word count is three and the percentage is 60%. The optional list shows the individual occurrence counts.
Different libraries and research projects use different stop-word lists. A word omitted from this tool’s list does not mean it can never be considered a stop word. The interface does not provide a custom-list editor.
The token pattern recognizes basic English letters, digits and underscores. It can split contractions, hyphenated expressions and accented words, which changes both matches and the denominator. It does not provide reliable Hindi or multilingual stop-word analysis.
Do not remove every match from a published sentence. Articles, pronouns and connecting words make prose understandable. Negation is particularly important: the built-in list includes “no” and “not”, and deleting them can reverse a statement’s meaning.
Use this counter to inspect a text-processing sample or compare drafts under the same rules. A high or low percentage is not a verdict on readability, originality or search performance, and the tool does not automatically rewrite your text.
Start by deciding whether you want to inspect a whole document, its main body or a short section. A copied webpage can include menus, disclaimers and repeated footer text, which changes the token total and the stop-word percentage. Select the passage relevant to your task and use the same boundaries when comparing versions. The counter does not identify main content or remove interface text automatically before applying its fixed vocabulary list.
If you are learning about text processing, a short sample is often more informative than a very large document. You can check each matched word against the original and see how repeated occurrences affect the result. For a longer comparison, record the total recognized tokens, stop-word occurrences and the chosen input scope. A percentage without those details can conceal whether the sample changed in length, subject or language between measurements.
Consider “the dog and the cat and the bird”. The repeated common words contribute several occurrences, even though the matched vocabulary contains only a small number of distinct entries. The headline stop-word total counts occurrences, while the optional list groups them by spelling. Do not describe the occurrence total as the number of different stop words in a passage; those are different quantities that answer different questions about the supplied text.
The percentage uses all recognized tokens as its denominator. If you add a paragraph of specialist terms without any listed stop words, the percentage can fall while the original stop-word occurrences remain unchanged. That is a ratio change, not necessarily an improvement in the writing. When explaining a result, show the count and denominator together so the person reading your analysis can distinguish a different mixture of text from actual removals of matched words.
There is no single universal list used by every text-processing system. A library may omit particular terms, add domain-specific words or preserve negation for a sentiment task. This page uses the list included in its script and does not offer a field for replacing it. If you compare its output with another system, compare the lists and token rules before assuming that unequal totals indicate an error in one of the tools.
A matching word is not automatically irrelevant. In a quotation, a grammatical exercise or a phrase someone is analyzing, a common word may be the subject of the discussion. The tool does not inspect that context; it simply compares tokens with a list. For a research or software workflow that needs reproducibility, keep the vocabulary definition and extraction rule with your results rather than reporting only that you “removed stop words” without specifying how.
The recognized tokens come from a basic pattern that includes English letters, digits and underscores. Punctuation can split forms that a reader regards as one word. A contraction may therefore be represented by parts, and those parts may or may not match the built-in list. This changes the denominator as well as the matching total. Inspect a few examples from your real input before treating the percentage as a comparable statistic across different writing styles.
The vocabulary is English and the tokenizer is not a reliable multilingual word segmenter. Hindi text or accented material may be omitted or divided unexpectedly, so a low stop-word percentage should not be interpreted as unusually information-rich prose. It may simply reflect unsupported input. For another language, choose an appropriate tokenizer and vocabulary together; translating only the stop-word list without checking the segmentation rule would leave an important part of the measurement unresolved.
Stop-word filtering can be part of some analytical workflows, but it should not be applied blindly to public-facing prose. Removing articles and pronouns often makes sentences awkward, while removing negation can reverse a claim. Because this list includes “no” and “not”, a downstream process that deletes every matched token needs particular care. This page counts matches; it does not decide whether deletion is appropriate for your task or automatically produce a filtered document.
For editing, read a sentence before changing any matched word and identify the actual problem you want to fix. If the issue is repetition, the Keyword Counter may help locate it more directly. If the issue is length, inspect redundant phrases rather than targeting all common words. Finish by checking that the text remains accurate and understandable. A smaller stop-word percentage is a numerical outcome, not a substitute for clear communication, a validated analysis method or a supported conclusion.
When using the stop words counter for an analysis exercise, keep the input sample and the list assumptions with the result. A label such as “common words removed” is too vague to explain a comparison if a second system uses another list. This page counts matches against its own vocabulary; it does not configure or validate a separate text-processing pipeline.
For repeated measurements, use the same input scope and review changes in both the recognized token total and the matching occurrences. That makes it easier to distinguish a changed ratio from a real change in the use of listed words. Keep grammatical and factual editing decisions grounded in the sentence meaning rather than in the percentage alone.
No. It counts occurrences. A matching word used five times adds five to the total; the optional list groups those occurrences by word.
The current interface uses its built-in English list and has no custom-list setting.
Not as a general editing rule. Many are necessary for grammar and meaning; removing negation can change a claim entirely.
No. It only describes matches under this tool’s token and list rules.
It lowercases the input before matching, so uppercase and lowercase versions of a listed word contribute to the same grouped count.
Yes. Use the list checkbox and recalculate. Hiding the list affects the displayed breakdown, not which occurrences contribute to the totals.
Yes. The built-in list includes “no” and “not”. Deleting those words in another workflow can change meaning, so do not treat every match as safe to remove.
It may use a different vocabulary, tokenizer or input selection. Compare those rules and denominators before interpreting the difference.
No. It reports counts and an optional grouped list. It does not rewrite the input into a filtered text.
No. The value depends on list coverage, tokenization and language as well as the writing. Review the actual information and clarity separately.