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Machine Learning Techniques for Text

You're reading from   Machine Learning Techniques for Text Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803242385
Length 448 pages
Edition 1st Edition
Languages
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Author (1):
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Nikos Tsourakis Nikos Tsourakis
Author Profile Icon Nikos Tsourakis
Nikos Tsourakis
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Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Introducing Machine Learning for Text 2. Chapter 2: Detecting Spam Emails FREE CHAPTER 3. Chapter 3: Classifying Topics of Newsgroup Posts 4. Chapter 4: Extracting Sentiments from Product Reviews 5. Chapter 5: Recommending Music Titles 6. Chapter 6: Teaching Machines to Translate 7. Chapter 7: Summarizing Wikipedia Articles 8. Chapter 8: Detecting Hateful and Offensive Language 9. Chapter 9: Generating Text in Chatbots 10. Chapter 10: Clustering Speech-to-Text Transcriptions 11. Index 12. Other Books You May Enjoy

Classifying Topics of Newsgroup Posts

The large volumes of unstructured text that large corporations and organizations need to sort daily necessitate automatizing tedious and time-consuming manual tasks. The good news is that machine learning (ML) is also of assistance when analyzing this type of data. This chapter will educate us on how to tag a text document using a list of predefined topics. The aim is to assign each sample to one and only one label, which becomes more challenging as the number of topics increases.

We will attack the problem by utilizing supervised and unsupervised ML techniques. First, we expand on the basic exploratory data analysis presented in the previous chapter and create richer visualizations with extra meaning and depth. The transformation of data from a high-dimensional space into a low-dimensional one assists in this task, so we will discuss pertinent techniques throughout the chapter. Then, we will implement two classifiers using one of Python’...

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