How to add custom stopwords and then remove them from text?
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How to add custom stopwords and then remove them from text?

How to add custom stopwords and then remove them from text?

This recipe helps you add custom stopwords and then remove them from text

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Recipe Objective

In a text or sentence, there are some words that do not contribute importance in the sentence or text, and we need to remove them. So there is a package called stopwords which is already present in the NLTK library that consists of the most commonly used words that should be removed from the text. But if we want to add our own custom list of words that we want to stop in our text or sentence, lets see how to make it.

Stopwords these are the words which does not add much meaning in the actual sentence or text, and they can be safely removed from the sentence or text. The words like the, is, have, has and many more can be removed.

Step 1 - Import nltk and download stopwords, and then import stopwords from NLTK

import nltk nltk.download('stopwords') from nltk.corpus import stopwords

Step 2 - lets see the stop word list present in the NLTK library, without adding our custom list

print(stopwords.words('english'))

['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn', "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn', "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", 'won', "won't", 'wouldn', "wouldn't"]

Step 3 - Create a Simple sentence

simple_text = "the city is beautiful, but due to traffic noice polution is increasing on daily basis which is hurting all the people"

Step 4 - Create our custom stopword list to add

new_stopwords = ["all", "due", "to", "on", "daily"]

Step 5 - add custom list to stopword list of nltk

stpwrd = nltk.corpus.stopwords.words('english') stpwrd.extend(new_stopwords)

Step 6 - download and import the tokenizer from nltk

nltk.download('punkt') from nltk.tokenize import word_tokenize

Step 7 - tokenizing the simple text by using word tokenizer

text_tokens = word_tokenize(simple_text)

Step 8 - Remove the custom stop words and print it

removing_custom_words = [words for words in text_tokens if not words in stpwrd] print(removing_custom_words)

['city', 'beautiful', ',', 'traffic', 'noice', 'polution', 'increasing', 'basis', 'hurting', 'people']

As we can see all custom words that we have added have been removed from our text.

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