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The Handbook of NLP with Gensim

The Handbook of NLP with Gensim

By : Chris Kuo
5 (6)
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The Handbook of NLP with Gensim

The Handbook of NLP with Gensim

5 (6)
By: Chris Kuo

Overview of this book

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.
Table of Contents (24 chapters)
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1
Part 1: NLP Basics
5
Part 2: Latent Semantic Analysis/Latent Semantic Indexing
9
Part 3: Word2Vec and Doc2Vec
12
Part 4: Topic Modeling with Latent Dirichlet Allocation
18
Part 5: Comparison and Applications

Comparing LDA/NMF/BERTopic on Twitter/X posts

The previous use case showed us the value of annotating social media posts. Since we have learned many other NLP techniques, can we apply these other techniques? In this use case, we will learn how they applied more techniques to the social media data.

Background

The richness of unstructured social media data has opened a new avenue for social science research. Topic modeling techniques have been applied to classify data and gain insights into it.

Questions

Similar to the previous use case on social media text, how can we apply NLP techniques to annotate a large number of social media posts?

NLP solution

The authors of [7] compared different types of topic modeling algorithms and documented their empirical findings. They collected 31,800 unique Twitter posts relating to travel and the COVID-19 pandemic. They applied Python LDA, NMF, and BERTopic modeling. They showed the standard results of these models, such as the topics...

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