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How to Use Snowball Stemmer NLTK package : Step by Step

Are you looking for step by step solution on How to use Snowball Stemmer NLTK package? Get complete Implementation of Snowball Stemmer in this article.

Using Snowball Stemmer NLTK-

 

Every stemmer converts words to its root form. But this stemmer word may or may not have meaning. This is the only difference between stemmers and lemmatizers. NLTK package provides various stemmers like PorterStemmer, Snowball Stemmer, and LancasterStemmer, etc. Still, PorterStemmer and Snowball Stemmer are more popular.

 

Step 1:

Firstly, we will import the NLTK’s SnowballStemmer package

from nltk.stem.snowball import SnowballStemmer

Step 2:

Secondly, Let’s create a language-specific object of the SnowballStemmer class

snowball_stemmer_obj = SnowballStemmer("english")

Step 3:

Let’s stemmerize any word using the above object-

var=snowball_stemmer_obj.stem("Programming")

Here is the complete output for the stemmerizer of Programming word.

 

Snowball stemmer Implementation in Python
Snowball stemmer Implementation in Python

 

Here you can see that the word “Programming” has a base word “program”. This base word is our Snowball stemmer for the programming word “Programming”.

 

How to Stemmerize complete sentence Using Snowball Stemmer NLTK

Like we have done above for words. Here we will Stemmerize complete sentences. Let’s see how –

Step 1:

Firstly, Import the stemmer package with a sentence tokenizer.

from nltk.stem.snowball import SnowballStemmer
from nltk.tokenize import word_tokenize

Step 2:

After it, Object creation of stemmer.

snowball_stemmer_obj = SnowballStemmer("english")

 

Step 3:

tokenized the text.

text = "Nature is the best teacher in the universe"
tokenize_text = word_tokenize(text)

Step 4:

In this step, We will iterate the tokens on a complete sentence. After it, we will find the stemmer words respectively. Once we have the stemmer word, we will form the new sentence. Here is the complete code for this.

sentence=""
for token in tokenize_text :
 var=snowball_stemmer_obj.stem(token)
 if(sentence==""):
  sentence=var
 else:
  sentence=sentence+" "+var
print(sentence)

 

In addition, Here is the output of the full code for sentence stemmerizer in python.

 

"<yoastmark

 

Most importantly, the Snowball stemmer Package supports multiple languages. Snowball stemmer package supports mainly English, french, german, Finnish, Spanish Swedish and dutch, etc languages.

 

Well, I hope you must like the easy implementation of the Snowball stemmer.  Still, if you have any doubts related to this topic, please comment below.

Thanks

Data Science Learner Team

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