The idea behind this chapter was to introduce you to the world of text mining. We want to give you a basic introduction to some of the most common algorithms available with text classification and clustering .We know how some of these concept will help you to build really great NLP applications, such as spam filters, domain centric news feeds, web page taxonomy, and so on. Though we have not used NLTK to classify the module in our code snippets, we used NLTK for all the preprocessing steps. We highly recommend you to use scikit-learn over NLTK for any classification problem. In this chapter, we started with machine learning and the types of problems that it can address. We discussed some of the specifics of ML problems in the context of text. We talked about some of the most common classification algorithms that are used for text classification, clustering, and topic modeling. We also give you enough implementation details to get the job done. I still think you need to read a lot...
NLTK Essentials
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NLTK Essentials
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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>
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Table of Contents (17 chapters)
NLTK Essentials
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Introduction to Natural Language Processing
Text Wrangling and Cleansing
Part of Speech Tagging
Parsing Structure in Text
NLP Applications
Text Classification
Web Crawling
Using NLTK with Other Python Libraries
Social Media Mining in Python
Text Mining at Scale
Index
Customer Reviews