NLTK Essentials

Front Cover
Packt Publishing, Jul 27, 2015 - Computers - 194 pages

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.

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.

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.

About the author (2015)

Nitin Hardeniya is a data scientist with more than 4 years of experience working with companies such as Fidelity, Groupon, and [24]7-inc. He has worked on a variety of business problems across different domains. He holds a master's degree in computational linguistics from IIIT-H. He is the author of 5 patents in the field of customer experience. He is passionate about language processing and large unstructured data. He has been using Python for almost 5 years in his day-to-day work. He believes that Python could be a single-point solution to most of the problems related to data science. He has put on his hacker's hat to write this book and has tried to give you an introduction to all the sophisticated tools related to NLP and machine learning in a very simplified form. In this book, he has also provided a workaround using some of the amazing capabilities of Python libraries, such as NLTK, scikit-learn, pandas, and NumPy.

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