Python - Remove Stopwords

Stop word is a generally used word ( similar as “ the”, “ a”, “ an”, “ in”) that a hunt machine has been programmed to ignore, both when indexing entries for searching and when reacquiring them as the result of a hunt query.

We'd not want these words to take up space in our database, or taking up precious processing time. For this, we can remove them fluently, by storing a list of words that you consider to stop words. NLTK (Natural Language Toolkit) in python has a list of stopwords stored in 16 different languages. You can find them in thenltk_data directory. home/ pratima/nltk_data/ corpora/ stopwords is the directory address. ( Don't forget to change your home directory name)

What are Stopwords?

Stopwords are the most common words in any natural language. For the ambition of assaying textbook data and fabricating NLP models, these stopwords might not add important value to the content of the document.

Generally, the most familiar words used in a textbook are “ the”, “ is”, “ in”, “ for”, “ where”, “ when”, “ to”, “ at” etc.

Consider this textbook string – “ There's a pen on the table”. currently, the words “ is”, “ a”, “ on”, and “ the” add no meaning to the statement while parsing it. Whereas words like “ there”, “ book”, and “ table” are the keywords and tell us what the statement is each about.



Why do we Need to Remove Stopwords?

Quite an important question and bone you must have in mind.

Removing stopwords isn't a hard and fast rule in NLP. It depends upon the task that we're working on. For tasks like textbook bracket, where the textbook is to be classified into different orders, stopwords are removed or closed out from the given textbook so that further focus can be contributed to those terms which define the meaning of the textbook.

Just like we saw in the below section, words like there, book, and table add further meaning to the textbook as compared to the words is and on.

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