Book Image

NLTK Essentials

By : Nitin Hardeniya
Book Image

NLTK Essentials

By: Nitin Hardeniya

Overview of this book

<p>Natural Language Processing (NLP) is the field of artificial intelligence and computational linguistics that deals with the interactions between computers and human languages. With the instances of human-computer interaction increasing, it’s becoming imperative for computers to comprehend all major natural languages. Natural Language Toolkit (NLTK) is one such powerful and robust tool.</p> <p>You start with an introduction to get the gist of how to build systems around NLP. We then move on to explore data science-related tasks, following which you will learn how to create a customized tokenizer and parser from scratch. Throughout, we delve into the essential concepts of NLP while gaining practical insights into various open source tools and libraries available in Python for NLP. You will then learn how to analyze social media sites to discover trending topics and perform sentiment analysis. Finally, you will see tools which will help you deal with large scale text.</p> <p>By the end of this book, you will be confident about NLP and data science concepts and know how to apply them in your day-to-day work.</p>
Table of Contents (17 chapters)
NLTK Essentials
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Summary


This chapter was a brief summary of some of the most fundamental libraries of Python that do a lot of heavy lifting for us when we deal with text and other data. NumPy helps us in dealing with numeric operations and the kind of data structure required for some of these. SciPy has many scientific operations that are used in various Python libraries. We learned how to use these functions and data structures.

We have also touched upon pandas, which is a very efficient library for data manipulation, and has been getting a lot of mileage in recent times. Finally, we gave you a quick view of one of Python's most commonly used visualization libraries, matplotlib.

In the next chapter, we will focus on social media. We will see how to capture data from some of the common social networks and produce meaningful insights around social media.