Best Python Libraries for NLP

Natural language processing (NLP) is a subfield of artificial intelligence and computer science. NLP is the process of processing and analyzing natural language data to create intelligent applications that can understand human languages. Like machine learning, Python is also preferred for natural language processing due to the vast collection of useful libraries for processing natural language data. So, if you are learning NLP and want to know the best Python libraries for NLP, this article is for you. In this article, I will introduce you to some of the best Python libraries for NLP that you should learn.

Best Python Libraries for NLP


NLTK is one of the best libraries for working with natural language data using Python. Whenever I work on a problem based on NLP, I always prefer using NLTK. This library comes with so many modules for solving various problems of NLP. You can use this library for text classification, sentiment analysis, tokenization, stemming, tagging, parsing, semantic reasoning and many more tasks of Natural Language Processing. You can learn more about this Python library here.


Textblob is not as popular as NLTK for Natural Language Processing, but it is also a very useful Python library that you should learn. It has all the features that NLTK provides, but one feature it has where NLTK lacks is spelling correction. If I have to choose Textblob over NLTK for any task of NLP, I will prefer it to check spellings in a piece of text. You can learn more about Textblob for Natural Language processing from here.


Spacy is one of the most popular Python libraries. It has all the features that other Python libraries have for natural language processing, but it supports all those features in 64+ languages which makes it a better library for NLP for working with different languages.

It also has an amazing Named Entity Recognition feature which aims to highlight named entities in a piece of text. Other NLP libraries also have functionality for named entity recognition, but they don’t have any functionality to highlight named entities for better representation. So I will prefer Spacy for Named Entity Recognition over other NLP libraries. You can learn more about Spacy here.


So NLTK, Spacy, and Textblob are the best Python libraries that you can use for NLP. You can choose any of these libraries. I prefer NLTK for almost all the problems of NLP other than spelling correction and Named Entity Recognition. For spelling correction, I prefer Textblob, and for Named Entity Recognition, I prefer Spacy. I hope you liked this article on the best Python libraries for NLP. Feel free to ask valuable questions in the comments section below.

Aman Kharwal
Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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