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Python Natural Language Processing Cookbook

You're reading from   Python Natural Language Processing Cookbook Over 60 recipes for building powerful NLP solutions using Python and LLM libraries

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781803245744
Length 312 pages
Edition 2nd Edition
Languages
Concepts
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Authors (2):
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Saurabh Chakravarty Saurabh Chakravarty
Author Profile Icon Saurabh Chakravarty
Saurabh Chakravarty
Zhenya Antić Zhenya Antić
Author Profile Icon Zhenya Antić
Zhenya Antić
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Toc

Table of Contents (13) Chapters Close

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: Visualizing Text Data 8. Chapter 8: Transformers and Their Applications 9. Chapter 9: Natural Language Understanding 10. Chapter 10: Generative AI and Large Language Models 11. Index 12. Other Books You May Enjoy

Visualizing topics from Gensim

In this recipe, we will visualize the Latent Dirichlet Allocation (LDA) topic model that we created in Chapter 6. The visualization will allow us to quickly see words that are most relevant to a topic and the distances between topics.

After working through this recipe, you will be able to load an existing LDA model and create a visualization for its topics, both in Jupyter and saved as an HTML file.

Getting ready

We will use the pyLDAvis package to create the visualization. It is available in the poetry environment and the requirements.txt file.

We will load the model we created in Chapter 6 and then use the pyLDAvis package to create the topic model visualization.

The notebook is located at https://github.com/PacktPublishing/Python-Natural-Language-Processing-Cookbook-Second-Edition/blob/main/Chapter07/7.6_topics_gensim.ipynb.

How to do it...

  1. Import the necessary packages and functions:
    import gensim
    import pyLDAvis.gensim
  2. ...
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