Removing stop words with NLTK in Python
The process of converting data to something a computer can understand is referred to as pre-processing. One of the major forms of pre-processing is to filter out useless data. In natural language processing, useless words [data], are referred to as stop words.
What are Stop words?
Stop Words: A stop word is a commonly used word [such as “the”, “a”, “an”, “in”] that a search engine has been programmed to ignore, both when indexing entries for searching and when retrieving them as the result of a search query.
We would not want these words to take up space in our database, or taking up valuable processing time. For this, we can remove them easily, 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 the nltk_data directory. home/pratima/nltk_data/corpora/stopwords is the directory address.[Do not forget to change your home directory name]
To check the list of stopwords you can type the following commands in the python shell.
import nltk from nltk.corpus import stopwords print[stopwords.words['english']]
{‘ourselves’, ‘hers’, ‘between’, ‘yourself’, ‘but’, ‘again’, ‘there’, ‘about’, ‘once’, ‘during’, ‘out’, ‘very’, ‘having’, ‘with’, ‘they’, ‘own’, ‘an’, ‘be’, ‘some’, ‘for’, ‘do’, ‘its’, ‘yours’, ‘such’, ‘into’, ‘of’, ‘most’, ‘itself’, ‘other’, ‘off’, ‘is’, ‘s’, ‘am’, ‘or’, ‘who’, ‘as’, ‘from’, ‘him’, ‘each’, ‘the’, ‘themselves’, ‘until’, ‘below’, ‘are’, ‘we’, ‘these’, ‘your’, ‘his’, ‘through’, ‘don’, ‘nor’, ‘me’, ‘were’, ‘her’, ‘more’, ‘himself’, ‘this’, ‘down’, ‘should’, ‘our’, ‘their’, ‘while’, ‘above’, ‘both’, ‘up’, ‘to’, ‘ours’, ‘had’, ‘she’, ‘all’, ‘no’, ‘when’, ‘at’, ‘any’, ‘before’, ‘them’, ‘same’, ‘and’, ‘been’, ‘have’, ‘in’, ‘will’, ‘on’, ‘does’, ‘yourselves’, ‘then’, ‘that’, ‘because’, ‘what’, ‘over’, ‘why’, ‘so’, ‘can’, ‘did’, ‘not’, ‘now’, ‘under’, ‘he’, ‘you’, ‘herself’, ‘has’, ‘just’, ‘where’, ‘too’, ‘only’, ‘myself’, ‘which’, ‘those’, ‘i’, ‘after’, ‘few’, ‘whom’, ‘t’, ‘being’, ‘if’, ‘theirs’, ‘my’, ‘against’, ‘a’, ‘by’, ‘doing’, ‘it’, ‘how’, ‘further’, ‘was’, ‘here’, ‘than’}
Note: You can even modify the list by adding words of your choice in the english .txt. file in the stopwords directory.
Removing stop words with NLTK
The following program removes stop words from a piece of text:
|
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
example_sent = """This is a sample sentence,
showing off the stop words filtration."""
stop_words = set[stopwords.words['english']]
word_tokens = word_tokenize[example_sent]
filtered_sentence = [w for w in word_tokens if not w.lower[] in stop_words]
filtered_sentence = []
for w in word_tokens:
if w not in stop_words:
filtered_sentence.append[w]
print[word_tokens]
print[filtered_sentence]
|
Output:
['This', 'is', 'a', 'sample', 'sentence', ',', 'showing', 'off', 'the', 'stop', 'words', 'filtration', '.'] ['This', 'sample', 'sentence', ',', 'showing', 'stop', 'words', 'filtration', '.']Performing the Stopwords operations in a file
In the code below, text.txt is the original input file in which stopwords are to be removed. filteredtext.txt is the output file. It can be done using following code:
|
import io
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
# word_tokenize accepts
# a string as an input, not a file.
stop_words = set[stopwords.words['english']]
file1 = open["text.txt"]
# Use this to read file content as a stream:
line = file1.read[]
words = line.split[]
for r in words:
if not r in stop_words:
appendFile = open['filteredtext.txt','a']
appendFile.write[" "+r]
appendFile.close[]
|
This is how we are making our processed content more efficient by removing words that do not contribute to any future operations.
This article is contributed by Pratima Upadhyay. If you like GeeksforGeeks and would like to contribute, you can also write an article using write.geeksforgeeks.org or mail your article to . See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.
Overview
- Learn how to remove stopwords and perform text normalization in Python – an essential Natural Language Processing [NLP] read
- We will explore the different methods to remove stopwords as well as talk about text normalization techniques like stemming and lemmatization
- Put your theory into practice by performing stopwords removal and text normalization in Python using the popular NLTK, spaCy and Gensim libraries
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Python - Remove Stopwords
Stopwords are the English words which does not add much meaning to a sentence. They can safely be ignored without sacrificing the meaning of the sentence. For example, the words like the, he, have etc. Such words are already captured this in corpus named corpus. We first download it to our python environment.
import nltk nltk.download['stopwords']It will download a file with English stopwords.
How to remove stop words in Python
In this article, you will learn different ways to remove stop words in Python. Python provides various modules to get the stop words.
In SEO phrasing, stop words [such as "the", "a", "an", "in", "at"] are the most widely recognized words that most web crawlers stay away from, for the reasons for sparing reality in preparing of huge information during slithering or ordering. This causes web indexes to spare space in their information bases.
Stop words are consistently eliminated from the substance before preparing deep learning and machine learning models since stop words occur in bounty, therefore giving essentially no phenomenal information that can be used for portrayal or gathering. A couple of instruments expressly avoid eliminating these stop words to help with a state search. We have to eliminate stop words while performing assignments, like Spam Filtering, Auto-Tag Generation, Language Classification, and so forth.
NLTK corpus: Remove stop words from a given text
Last update on February 26 2020 08:09:22 [UTC/GMT +8 hours]A handy guide about English stop words removal in Python
We are well aware of the fact that computers can easily process numbers if programmed well. 🧑🏻💻 However, a large portion of the information we have is in the form of text. 📗 We communicate with each other by directly talking with them or using text messages, social media posts, phone calls, video calls, etc. In order to create intelligent systems, we need to use this information that we have in abundance.
Natural Language Processing [NLP] is the branch of Artificial Intelligence that allows machines to interpret human language. 👍🏼 However, the same cannot be used directly by the machine, and we need to pre-process the same first.
Text pre-processing is the process of preparing text data so that machines can use the same to perform tasks like analysis, predictions, etc. There are many different steps in text pre-processing but in this article, we will only get familiar with stop words, why do we remove them, and the different libraries that can be used to remove them.
So, let’s get started. 🏃🏽♀️