Introduction
This article discusses the theoretical knowledge about Lemmatization with TextBlob. TextBlob strives to provide a familiar interface for typical text-processing operations. TextBlob objects can be treated as if they were Python strings that had mastered Natural Language Processing. Although NLTK provides certain ways for completing those jobs, you may need to call many classes to fulfill various tasks. However, using TextBlob, all you have to do is use TextBlob(text) to access all of TextBlob's methods!
Lemmatization
Lemmatization is the process of combining a word's several inflected forms into a single item that can be studied. Lemmatization is similar to stemming, but it gives the context of the words. As a result, it connects words with similar meanings into a single term.
Both stemming and lemmatization are included in text preprocessing. These two terms are often misunderstood by people. Some people confuse these two. In fact, lemmatization is preferable to stemming since it does the morphological examination of the words.
Lemmatization can be found in a variety of places, including search engines and other thorough retrieval systems, and Compact indexing.
CODE
# importing library
from textblob import Word
# create object word.
u_new= Word("rocks")
# implementing lemmatization.
print("rocks =", u.lemmatize())
# create object Word.
v_new = Word("corpora")
# implementing lemmatization.
print("corpora =", v.lemmatize())
# create object word.
w_new = Word("better")
# implementing lemmatization with parameter "a=adjective"
print("better =", w.lemmatize("a"))
OUTPUT
rocks = rock
corpora = corpus
better = good