Stop words are the small, very common words that hold sentences together without saying much on their own: the, and, of, is, to, in. Take them out and what remains is the skeleton of the text, which is often all you need if you are trying to see what a passage is about, build a tag list, or prepare text for analysis.
Paste your text above. The stop words disappear from the result, and the stats tell you how many were removed and how many words remain.
“The quick brown fox is jumping over the lazy dog and it was not happy about that” becomes “quick brown fox jumping lazy dog happy”. Which brings up the most important thing about this tool.
It removes “not”, and that matters
The list includes negations like “not” and “no”, plus words like “can”, “would” and “up”. So “not happy” turns into “happy”, which says the opposite. For counting and keyword discovery that does not matter. For anything where meaning counts, such as sentiment, summaries or a sentence a person will read, do not use the output as written text. It is a working copy for machines and for your own eyes, not something to publish.
What is on the list
Around a hundred of the most frequent English words: articles (a, an, the), conjunctions (and, or, but), prepositions (of, to, in, on, at, by, for, with, from), pronouns (I, you, he, she, we, they and their forms), and common verbs like is, are, was, have, do, can and will. The list does not change and you cannot edit it. For a custom set, find and replace with a regular expression like \b(word1|word2)\b does the same job with your own words.
Everything is matched without regard to case. Contractions such as “don’t” are treated as one word and, since they are not on the list, they stay.
Good ways to use it
Finding what a document is really about: strip the stop words, run the result through the word frequency counter, and the most common remaining words are usually the topic. The keyword density checker does this in one step if you want phrases as well as single words. Building tags and hashtags from a title is another use; the hashtag generator has its own option to drop small words.
If your input still has punctuation stuck to words, remove punctuation first so “the.” and “the” are recognised as the same thing. And if the output is full of repeats, remove duplicate words will boil it down to a unique list.