The Handbook of NLP with Gensim: Leverage topic modeling to uncover hidden patterns, themes, and valuable insights within textual data

Navigating the terrain of NLP research and applying it practically can be a formidable task made easy with The Handbook of NLP with Gensim. This book demystifies NLP and equips you with hands-on strategies spanning healthcare, e-commerce, finance, and more to enable you to leverage Gensim in real-world scenarios.
You’ll begin by exploring motives and techniques for extracting text information like bag-of-words, TF-IDF, and word embeddings. This book will then guide you on topic modeling using methods such as Latent Semantic Analysis (LSA) for dimensionality reduction and discovering latent semantic relationships in text data, Latent Dirichlet Allocation (LDA) for probabilistic topic modeling, and Ensemble LDA to enhance topic modeling stability and accuracy.
Next, you’ll learn text summarization techniques with Word2Vec and Doc2Vec to build the modeling pipeline and optimize models using hyperparameters. As you get acquainted with practical applications in various industries, this book will inspire you to design innovative projects. Alongside topic modeling, you’ll also explore named entity handling and NER tools, modeling procedures, and tools for effective topic modeling applications.
By the end of this book, you’ll have mastered the techniques essential to create applications with Gensim and integrate NLP into your business processes.

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The Handbook of NLP with Gensim: Leverage topic modeling to uncover hidden patterns, themes, and valuable insights within textual data

Navigating the terrain of NLP research and applying it practically can be a formidable task made easy with The Handbook of NLP with Gensim. This book demystifies NLP and equips you with hands-on strategies spanning healthcare, e-commerce, finance, and more to enable you to leverage Gensim in real-world scenarios.
You’ll begin by exploring motives and techniques for extracting text information like bag-of-words, TF-IDF, and word embeddings. This book will then guide you on topic modeling using methods such as Latent Semantic Analysis (LSA) for dimensionality reduction and discovering latent semantic relationships in text data, Latent Dirichlet Allocation (LDA) for probabilistic topic modeling, and Ensemble LDA to enhance topic modeling stability and accuracy.
Next, you’ll learn text summarization techniques with Word2Vec and Doc2Vec to build the modeling pipeline and optimize models using hyperparameters. As you get acquainted with practical applications in various industries, this book will inspire you to design innovative projects. Alongside topic modeling, you’ll also explore named entity handling and NER tools, modeling procedures, and tools for effective topic modeling applications.
By the end of this book, you’ll have mastered the techniques essential to create applications with Gensim and integrate NLP into your business processes.

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The Handbook of NLP with Gensim: Leverage topic modeling to uncover hidden patterns, themes, and valuable insights within textual data

The Handbook of NLP with Gensim: Leverage topic modeling to uncover hidden patterns, themes, and valuable insights within textual data

by Chris Kuo
The Handbook of NLP with Gensim: Leverage topic modeling to uncover hidden patterns, themes, and valuable insights within textual data
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The Handbook of NLP with Gensim: Leverage topic modeling to uncover hidden patterns, themes, and valuable insights within textual data

The Handbook of NLP with Gensim: Leverage topic modeling to uncover hidden patterns, themes, and valuable insights within textual data

by Chris Kuo

Overview

Navigating the terrain of NLP research and applying it practically can be a formidable task made easy with The Handbook of NLP with Gensim. This book demystifies NLP and equips you with hands-on strategies spanning healthcare, e-commerce, finance, and more to enable you to leverage Gensim in real-world scenarios.
You’ll begin by exploring motives and techniques for extracting text information like bag-of-words, TF-IDF, and word embeddings. This book will then guide you on topic modeling using methods such as Latent Semantic Analysis (LSA) for dimensionality reduction and discovering latent semantic relationships in text data, Latent Dirichlet Allocation (LDA) for probabilistic topic modeling, and Ensemble LDA to enhance topic modeling stability and accuracy.
Next, you’ll learn text summarization techniques with Word2Vec and Doc2Vec to build the modeling pipeline and optimize models using hyperparameters. As you get acquainted with practical applications in various industries, this book will inspire you to design innovative projects. Alongside topic modeling, you’ll also explore named entity handling and NER tools, modeling procedures, and tools for effective topic modeling applications.
By the end of this book, you’ll have mastered the techniques essential to create applications with Gensim and integrate NLP into your business processes.

Product Details

ISBN-13: 9781803245508
Publisher: Packt Publishing
Publication date: 10/27/2023
Sold by: Barnes & Noble
Format: eBook
Pages: 310
File size: 7 MB

About the Author

Chris Kuo is a data scientist with over 23 years of experience. He led various data science solutions including customer analytics, health analytics, fraud detection, and litigation. He is also an inventor of a U.S. patent. He has worked at several Fortune 500 companies in the insurance and retail industries. Chris teaches at Columbia University and has taught at Boston University and other universities. He has published articles in economic and management journals and served as a journal reviewer. He is the author of the eXplainable A.I., Modern Time Series Anomaly Detection, Transfer Learning for Image Classification, and The Handbook of Anomaly Detection. He received his undergraduate degree in Nuclear Engineering and Ph.D. in Economics.

Table of Contents

Table of Contents
  1. Introduction to NLP
  2. Word Embedding
  3. Text Wrangling and Preprocessing
  4. Latent Semantic Analysis with scikit-learn
  5. Cosine Similarity
  6. Latent Semantic Indexing with Gensim
  7. Using Word2Vec
  8. Doc2Vec with Gensim
  9. Understanding Discrete Distributions
  10. Latent Dirichlet Allocation
  11. LDA Modeling
  12. LDA Visualization
  13. The Ensemble LDA for Model Stability
  14. LDA and BERTopic
  15. Real-World Use Cases
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