Pre-processing is the process of transforming data into something that a computer can understand. Filtering out worthless data is a common type of pre-processing. In natural language processing, stop words are worthless (useless) words (data).
Stop Words:
A stop word is a regularly used term for example, “the,” “a,” “an,”,”is” or “in” that a search engine has been configured to ignore, both while indexing entries for searching and retrieving them as the result of a search query.
We don’t want these terms taking up space in our database or using precious processing time. We can easily eliminate them by storing a list of terms that you believe to stop words. Python’s NLTK (Natural Language Toolkit) contains a list of stopwords in 16 different languages. You may find them in the nltk data directory, which is located at home/folder/nltk data/corpora/stopwords.
Note: Don’t forget to modify the name of your home directory.
Before going to the coding part, download the corpus including stop words from the NLTK module.
# Import nltk module using the import keyword.
import nltk
# Pass the 'stopwords' as an argument to the download() function to download all the
# stop words package
nltk.download('stopwords')
Output:
[nltk_data] Downloading package stopwords to /root/nltk_data... [nltk_data] Unzipping corpora/stopwords.zip. True
Printing the stop words list from the corpus:
# Import stopwords from nltk.corpus using the import keyword.
from nltk.corpus import stopwords
# Print all the stopwords in english language using the words() function in
# stopwords.
print(stopwords.words('english'))
Output:
['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"]
You can also select stopwords from the other languages based on requirements.
Get all the Languages list that can be used:
The below are the languages that are available in the NLTK ‘stopwords’ corpus.
# Import stopwords from nltk.corpus using the import keyword. from nltk.corpus import stopwords # Get all the Languages list that can be used using the fileids() function in # stopwords print(stopwords.fileids())
Output:
['arabic', 'azerbaijani', 'bengali', 'danish', 'dutch', 'english', 'finnish', 'french', 'german', 'greek', 'hungarian', 'indonesian', 'italian', 'kazakh', 'nepali', 'norwegian', 'portuguese', 'romanian', 'russian', 'slovene', 'spanish', 'swedish', 'tajik', 'turkish']
Adding our own stop words to the corpus:
# Import stopwords from nltk.corpus using the import keyword.
from nltk.corpus import stopwords
# Get all the stopwords in english language using the words() function in
# stopwords.
# Store it in a variable
our_stopwords = stopwords.words('english')
# Append some random stop word to the above obtained stopwords list using the
# append() function
our_stopwords.append('forexample')
print(our_stopwords)
Output:
['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", 'forexample']
The user-given stop word is added at the end. Check it out in the Output.
Removal of stop words:
The below is the code for removing all the stop words from a random string/sentence.
Tokenization:
Tokenization is the process of converting a piece of text into smaller parts known as tokens. These tokens are the core of NLP.
Tokenization is used to convert a sentence into a list of words.
Approach:
- Import word_tokenize from nltk.tokenize using the import keyword.
-
Import stopwords from nltk.corpus using the import keyword.
-
Download ‘stopwords’,’punkt’ from nltk module using the download() function.
-
Import word_tokenize from nltk.tokenize using the import keyword.
- Give the random string as static input and store it in a variable.
- Pass the given string to the word_tokenize() function to convert the given string into a list of words.
-
Remove the stop words from the given string using the list comprehension and store it in another variable.
-
Print the string after removing stopwords.
- The Exit of the Program.
Below is the implementation:
# Import nltk module using the import keyword.
import nltk
# Import stopwords from nltk.corpus using the import keyword.
from nltk.corpus import stopwords
# Download 'stopwords','punkt' from nltk module using the download() function.
nltk.download('stopwords')
nltk.download('punkt')
# Import word_tokenize from nltk.tokenize using the import keyword.
from nltk.tokenize import word_tokenize
# Give the random string as static input and store it in a variable.
gvn_str = "hello this is btechgeeks in is good morning all is a"
# Pass the given string to the word_tokenize() function to convert the given
# string into a list of words.
text_tokens = word_tokenize(gvn_str)
# Remove the stop words from the given string using the list comprehensionÂ
# and store it in another variable.
stopwrds_removd = [word for word in text_tokens if not word in stopwords.words()]
# Print the string after removing stopwords.
print(stopwrds_removd)
Output:
['hello', 'btechgeeks', 'good', 'morning']