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Text Mining with R: A Tidy Approach
Ebook Free Text Mining with R: A Tidy Approach
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About the Author
Julia Silge is a data scientist at Stack Overflow; her work involves analyzing complex datasets and communicating about technical topics with diverse audiences. She has a PhD in astrophysics and loves Jane Austen and making beautiful charts. Julia worked in academia and ed tech before moving into data science and discovering the statistical programming language R.David Robinson is a data scientist at Stack Overflow with a PhD in Quantitative and Computational Biology from Princeton University. He enjoys developing open source R packages, including broom, gganimate, fuzzyjoin and widyr, as well as blogging about statistics, R, and text mining on his blog, Variance Explained.
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Product details
Paperback: 194 pages
Publisher: O'Reilly Media; 1 edition (July 2, 2017)
Language: English
ISBN-10: 1491981652
ISBN-13: 978-1491981658
Product Dimensions:
6.9 x 0.4 x 9.1 inches
Shipping Weight: 12.6 ounces (View shipping rates and policies)
Average Customer Review:
4.6 out of 5 stars
16 customer reviews
Amazon Best Sellers Rank:
#48,353 in Books (See Top 100 in Books)
Great book. Am just finishing up the coursera data science capstone. This book and the code it contains was the basis for creating and filtering the ngrams I used for my capstone. Wish it helped a bit with more very large data sets(Data.Table's) but TidyText did consume and analyze the entire capstone two giga byte data set (just let it run for a while). If your heading into the capstone this is a great starting point on generating and analyzing that data set. Nice work.
This is a delightful book on practical textual mining. While it is not targeted at someone just starting out in R, anyone with intermediate knowledge will find this a precious gold mine! :-) It details how to compare texts from different authors, how to graph word connections, word correlations, apply clustering to texts, and so on.It's a short book, but it's worth it's weight in gold. I'm already reading this a second time!
Great Book with sample codes and simple explanation for complex problems.
Small book but crammed with info. I have stuck my nose in it everyday since getting it and have loved every minute. I’m glad I bought the hard copy as I can’t get on WiFi at work and since it’s such a lightweight book it’s very easy to carryPro tip: I would recommend brushing up on your tidyverse before diving in. This should help make understanding what the authors are trying to do much easier.
This is a delightful book on textual mining with R.
very comprehensive and easy to use.
This is an excellent book about text mining. I would highlight not only the clear and concise explanation given by the authors, but also the relevance of the examples and the structure of the book. Furthermore, it is extremely important that the authors base their analyses on the tidy approach to data analysis (a framework of concepts that is rapidly becoming the standard approach in R). The best chapters are the three fleshed out examples in the last chapters.I have only two complaints: the book could be longer; some mathematical treatment could be introduced in the appendices as technical material. Nevertheless, these are extremely minor given how much I liked the book.Highly recommended!
Very very good. I am just a starter with R and this book is fantastic for text mining. I very strongly recommend for those who would like to get their hands into text mining.
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