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Natural Language Processing Fundamentals

You're reading from   Natural Language Processing Fundamentals Build intelligent applications that can interpret the human language to deliver impactful results

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Product type Paperback
Published in Mar 2019
Publisher
ISBN-13 9781789954043
Length 374 pages
Edition 1st Edition
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Authors (2):
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Dwight Gunning Dwight Gunning
Author Profile Icon Dwight Gunning
Dwight Gunning
Sohom Ghosh Sohom Ghosh
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Sohom Ghosh
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Table of Contents (10) Chapters Close

Preface 1. Introduction to Natural Language Processing FREE CHAPTER 2. Basic Feature Extraction Methods 3. Developing a Text classifier 4. Collecting Text Data from the Web 5. Topic Modeling 6. Text Summarization and Text Generation 7. Vector Representation 8. Sentiment Analysis Appendix

Introduction

In the previous chapter, we learned about different ways to collect data from local files and online resources. In this chapter, we will focus on topic modeling, which is a popular concept within natural language processing. Topic modeling is a simple way to capture meaning from a collection of documents. Note that, in this case, documents are any coherent collection of words, which could be as short as a tweet or as long as an article, based on the project at hand.

A topic model captures information about the concepts contained in a set of texts. Using these concepts, documents can be organized into different categories or topics.

Topic modeling is mostly done using unsupervised learning algorithms, which detect topics on their own. Topic modeling algorithms operate by doing statistical analysis of words or tokens in documents, and then they use those statistics to automatically assign documents to topics.

In this chapter, we will look at a few popular topic...

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