How do preprocessing steps such as tokenization, stemming, and removing stop words affect predictive models?
Build beginning-to-end workflows for predictive modeling using text as features
Compare traditional machine learning methods and deep learning methods for text data
Emil Hvitfeldt is a clinical data analyst working in healthcare, and an adjunct professor at American University where he is teaching statistical machine learning with tidymodels. He is also an open source R developer and author of the textrecipes package.
Julia Silge is a data scientist and software engineer at RStudio PBC where she works on open source modeling tools. She is an author, an international keynote speaker and educator, and a real-world practitioner focusing on data analysis and machine learning practice.
Title: Supervised Machine Learning for Text Analysis in R (Chapman & Hall/CRC Data Science Series)
Author:
ISBN: 9780367554194
Binding:
Publisher: Taylor & Francis Ltd
Publication Date: 2021-10-22
Number of Pages: 402
Weight: 0.7302 kg
I find this book very useful, as predictive modelling with text is an important field in data science and statistics, and yet the one that has been consistently under-represented in technical literature. Given the growing volume, complexity and accessibility of unstructured data sources, as well as the rapid development of NLP algorithms, knowledge and skills in this domain is in increasing demand. In particular, there's a demand for pragmatic guidelines that offer not just the theoretical background to the NLP issues but also explain the end-to-end modelling process and good practices supported with code examples, just like Supervised Machine Learning for Text Analysis in R does. Data scientists and computational linguists would be a prime audience for this kind of publication and would most likely use it as both, (coding) reference and a textbook.
~Kasia Kulma, data science consultant
This book fills a critical gap between the plethora of text mining books (even in R) that are too basic for practical use and the more complex text mining books that are not accessible to most data scientists. In addition, this book uses statistical techniques to do text mining and text prediction and classification. Not all text mining books take this approach, and given the level of this book, it is one of its strongest features.
~Carol Haney, Quatrics
This book would be valuable for advanced undergraduates and early PhD students in a wide range of areas that have started using text as data...The main strength of the book is its connection to the tidyverse environment in R. It's relatively easy to pick up and do powerful things.
~David Mimno, Cornell University
The authors do a great job of presenting R programmers a variety of deep learning applications to text-based problems. Perhaps one of the best parts of this book is the section on interpretability, where the authors showcase methods to diagnose features on which these complex models rely to make their prediction. Considering how important the area of interpretability is to natural language processing research and is often skipped in applied textbooks, the authors should be commended for incorporating it in this book.
~Kanishka Misra, Purdue University
In conclusion, the presented book is extremely useful for graduate students, advanced researchers, and practitioners of statistics and data science who are interested in learning cutting-edge supervised ML techniques for text data. By utilizing the tidyverse environment and providing easy-to-understand R code examples with detailed study cases of real-world text mining problems, this book stands out and is a worthwhile read.
-Han-Ming Wu, National Chengchi University, Biometrics, September 2022
