Supervised Machine Learning for Text Analysis in R

Reviews / Votes

"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

"The volume is a valuable methodological resource, primarily for students interested in data science, concerned with: understanding the fundamentals of preprocessing steps required to transform a corpus, not always large, into a structure that is a good fit for modeling; implementation of machine learning and deep learning algorithms for building text predictive models under given research contexts in which they have to be integrated."
-Anca Vitcu ?in ?ISCB Book Reviews, ?September 2022