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Python Natural Language Processing Cookbook

You're reading from Python Natural Language Processing Cookbook Over 50 recipes to understand, analyze, and generate text for implementing language processing tasks

Product type Paperback

Published in Mar 2021

Publisher Packt

ISBN-13 9781838987312

Length 284 pages

Edition 1st Edition

Languages

Processing

Tools

Processing

Concepts

Mobile Application Development

Author (1):

Zhenya Antić

View More author details

Table of Contents (10) Chapters

Preface

1. Chapter 1: Learning NLP Basics

2. Chapter 2: Playing with Grammar FREE CHAPTER

3. Chapter 3: Representing Text – Capturing Semantics

4. Chapter 4: Classifying Texts

5. Chapter 5: Getting Started with Information Extraction

6. Chapter 6: Topic Modeling

7. Chapter 7: Building Chatbots

8. Chapter 8: Visualizing Text Data

9. Other Books You May Enjoy

LDA topic modeling with sklearn

In this recipe, we will use the LDA algorithm to discover topics that appear in the BBC dataset. This algorithm can be thought of as dimensionality reduction, or going from a representation where words are counted (such as how we represent documents using CountVectorizer or TfidfVectorizer, see Chapter 3, Representing Text: Capturing Semantics, we instead represent documents as sets of topics, each topic with a weight. The number of topics is of course much smaller than the number of words in the vocabulary. To learn more about how the LDA algorithm works, see https://highdemandskills.com/topic-modeling-intuitive/.

Getting ready

We will use the sklearn and pandas packages. If you haven't installed them, do so using the following command:

pip install sklearn
pip install pandas

How to do it…

We will use a dataframe to parse in the data, then represent the documents using the CountVectorizer object, apply the LDA algorithm, and...

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Authors (1)

Zhenya Antić

Zhenya Antić

Zhenya Antić, Ph.D. is an expert in AI and NLP. She is currently the Director of AI Automation at Arch Insurance, where she leads initiatives in Intelligent Document Processing and applies various AI solutions to complex problems. With extensive consulting experience, Zhenya has worked on numerous NLP projects with various companies. She holds a Ph.D. in Linguistics from the University of California, Berkeley, and a B.S. in Computer Science from the Massachusetts Institute of Technology.

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