Python for NLP: Introduction to the TextBlob Library

Introduction

This is the seventh article in my series of articles on Python for NLP. In my previous article, I explained how to perform topic modeling using Latent Dirichlet Allocation and Non-Negative Matrix factorization. We used the Scikit-Learn library to perform topic modeling.

In this article, we will explore TextBlob, which is another extremely powerful NLP library for Python. TextBlob is built upon NLTK and provides an easy to use interface to the NLTK library. We will see how TextBlob can be used to perform a variety of NLP tasks ranging from parts-of-speech tagging to sentiment analysis, and language translation to text classification.

The detailed download instructions for the library can be found at the official link. I would suggest that you install the TextBlob library as well as the sample corpora.

Here is the gist of the instructions linked above, but be sure to check the official documentation for more instructions on installing if you need it:

$ pip install -U textblob

And to install the corpora:

$ python -m textblob.download_corpora

Let’s now see the different functionalities of the

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