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Python Feature Engineering Cookbook

You're reading from  Python Feature Engineering Cookbook

Product type Book
Published in Jan 2020
Publisher Packt
ISBN-13 9781789806311
Pages 372 pages
Edition 1st Edition
Languages
Author (1):
Soledad Galli Soledad Galli
Profile icon Soledad Galli
Toc

Table of Contents (13) Chapters close

Preface 1. Foreseeing Variable Problems When Building ML Models 2. Imputing Missing Data 3. Encoding Categorical Variables 4. Transforming Numerical Variables 5. Performing Variable Discretization 6. Working with Outliers 7. Deriving Features from Dates and Time Variables 8. Performing Feature Scaling 9. Applying Mathematical Computations to Features 10. Creating Features with Transactional and Time Series Data 11. Extracting Features from Text Variables 12. Other Books You May Enjoy

Estimating text complexity by counting sentences

One aspect of a text we can capture in features is its complexity. Usually, longer descriptions that contain multiple sentences spread over several paragraphs tend to provide more information than descriptions with very few sentences. Therefore, capturing the number of sentences may provide some insight into the amount of information provided by the text. This process is called sentence tokenization. Tokenization is the process of splitting a string into a list of pieces or tokens. In the previous Counting characters, words, and vocabulary recipe, we did word tokenization, that is, we divided the string into words. In this recipe, we will divide the string into sentences and then we will count them. We will use the NLTK Python library, which provides this functionality.

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