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Table of contents
1.
Introduction
2.
Lemmatization
2.1.
OUTPUT
3.
FAQs
4.
Key Takeaways
Last Updated: Mar 27, 2024
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Lemmatization with TextBlob

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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
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FAQs

1. Which algorithm does TextBlob use?

One of the best features of TextBlob is that it lets you choose the algorithm to use to accomplish high-level NLP tasks: PatternAnalyzer is a built-in default classifier for the pattern library. NaiveBayesAnalyzer is an NLTK model that was trained on a corpus of movie reviews.

2. Which is better TextBlob or Vader?

Both libraries have a lot of features, so try running some sample data on your subject to determine which one performs the best for your needs. In my tests, VADER tends to do better with slang, emoticons, and another non-traditional language, whereas TextBlob excels with more formal language.

3. How does TextBlob calculate sentiment?

When computing a sentiment for a single word, TextBlob employs the "averaging" technique, which is applied to polarity values to calculate a polarity score for a single word, and thus a similar process is applied to every single word, resulting in a combined polarity for larger texts.

4. Is TextBlob rule-based?

Lexicons are another name for these rules. As a result, the Lexicon-based approach is known as the Rule-based approach. TextBlob, VADER, and SentiWordNet are some of the most widely utilised lexicon-based techniques.

Key Takeaways

So that's the end of the article.

In this article, we have extensively discussed the Extreme Gradient Boosting Machine and its implementation in python.

Isn't Machine Learning exciting!! We hope that this blog has helped you enhance your knowledge regarding Lemmatization with TextBlob and if you would like to learn more, check out our articles on MACHINE LEARNING COURSE. Do upvote our blog to help other ninjas grow. Happy Coding!

 

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