The NLTK library in Python is one of the best Python libraries for any natural language processing task. It provides so many useful functions for word processing including tokenization, stemming, tagging, parsing and many other tasks that we need to create machine learning models for any natural language processing application. In this article, I will take you to a tutorial on NLTK using Python.
What is NLTK in Python?
NLTK is a Python library that can be used in any natural language processing application. From converting textual data to building an NLP based application like sentiment analyzer, named entity recognition, etc. everything can be done using the NLTK library in Python. Being a leading framework in Python for creating application of NLP, the NLTK library is used by several companies in their applications including companies like:
- Shelf
- Quezx
- Autonom8
- Botanalytics
- Bunch
- Tech Stacks and many more.
To create an NLP application by using the NLTK library in Python you should have Python 3.5 or above. You can easily install this library by using the pip command; pip install nltk. Now in the section below, I will take you through a tutorial on NLTK using Python.
NLTK in Python (Tutorial)
Before creating any application based on natural language processing we need to process the data we are using. Below are some of the steps that are always necessary while creating an application of NLP:
- Tokenization: Splitting a piece of text into tokens or words is known as tokenization.
- Stopwords removal: Stopwords are the words that are the most common in any language. There is no proper definition of stopwords, you can think of these words as the words that are used to frame a meaningful sentence. For example, words like “the”, “is”, “a”, “as”, are some type of stopwords that needs to be removed from the textual data you are using, otherwise it may affect the performance of your model.
Below is how you can perform the task of tokenization and stopwords removal by using the NLTK library in Python:
Besides tokenization and stop word removal, there are still many tasks to be done to prepare the text data to create an application based on natural language processing. But these tasks depend on the type of application you are working on, while Tokenization and stopwords removal are always necessary when working with text data.
After learning the art of tokenization and stopwords removal by using the NLTK library in Python you can try working on some of the applications mentioned below to learn how to create NLP applications using Python:
Summary
I hope this tutorial on the NLTK library in Python has helped you to understand why the NLTK library is used in Python. In short, if you want to build any type of application based on natural language processing you can use it from basic text processing to creating a machine learning model. I hope you liked this article on a tutorial on the NLTK library in Python. Feel free to ask your valuable questions in the comments section below.
