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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
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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

Getting the dataset and evaluation ready

In this recipe, we will load a dataset, prepare it for processing, and create an evaluation baseline. This recipe builds on some of the recipes from Chapter 3, where we used different tools to represent text in a computer-readable form.

Getting ready

For this recipe, we will use the Rotten Tomatoes reviews dataset, available through Hugging Face. This dataset consists of user movie reviews that can be classified into positive and negative. We will prepare the dataset for machine learning classification. The preparation process in this case will involve loading the reviews, filtering out non-English language ones, tokenizing the text into words, and removing stopwords. Before the machine learning algorithm can run, the text reviews need to be transformed into vectors. This transformation process is described in detail in Chapter 3.

The notebook is located at https://github.com/PacktPublishing/Python-Natural-Language-Processing-Cookbook...

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