1. Language and modeling. 2. Tokenization. 3. Stop words. 4. Stemming. 5. Word Embeddings. 6. Regression. 7. Classification. 8. Dense neural networks. 9. Long short-term memory (LSTM) networks. 10. Convolutional neural networks.
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.
"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