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The Handbook of NLP with Gensim

You're reading from   The Handbook of NLP with Gensim Leverage topic modeling to uncover hidden patterns, themes, and valuable insights within textual data

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781803244945
Length 310 pages
Edition 1st Edition
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Author (1):
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Chris Kuo Chris Kuo
Author Profile Icon Chris Kuo
Chris Kuo
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Toc

Table of Contents (24) Chapters Close

Preface 1. Part 1: NLP Basics
2. Chapter 1: Introduction to NLP FREE CHAPTER 3. Chapter 2: Text Representation 4. Chapter 3: Text Wrangling and Preprocessing 5. Part 2: Latent Semantic Analysis/Latent Semantic Indexing
6. Chapter 4: Latent Semantic Analysis with scikit-learn 7. Chapter 5: Cosine Similarity 8. Chapter 6: Latent Semantic Indexing with Gensim 9. Part 3: Word2Vec and Doc2Vec
10. Chapter 7: Using Word2Vec 11. Chapter 8: Doc2Vec with Gensim 12. Part 4: Topic Modeling with Latent Dirichlet Allocation
13. Chapter 9: Understanding Discrete Distributions 14. Chapter 10: Latent Dirichlet Allocation 15. Chapter 11: LDA Modeling 16. Chapter 12: LDA Visualization 17. Chapter 13: The Ensemble LDA for Model Stability 18. Part 5: Comparison and Applications
19. Chapter 14: LDA and BERTopic 20. Chapter 15: Real-World Use Cases 21. Assessments 22. Index 23. Other Books You May Enjoy

Reviewing the results of BERTopic

Now, we are ready to inspect the outcome. We want to inspect the outcome of a topic model by inspecting the topic keywords and the count distribution across topics and show the representative documents for a topic. The BERTopic module has several convenient functions. They are as follows:

  • .get_topic_info(): Get all topic information
  • .get_topic_freq(): Get topic frequency
  • .get_topic(topic=12): Access a single topic, such as Topic 12
  • .get_topics(): Access all topics
  • .get_document_info(docs): Get all document information
  • .get_representative_docs(): Get representative docs per topic

Getting the topic information

First, I am going to inspect the topic information by using .get_topic_info():

topic_model.get_topic_info()[0:20]

The top 20 topics are shown in Table 15.1:

Topic

Count

Name

%

...
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