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Product details

File Size: 14379 KB

Print Length: 196 pages

Simultaneous Device Usage: Unlimited

Publisher: O'Reilly Media; 1 edition (June 12, 2017)

Publication Date: June 12, 2017

Sold by: Amazon Digital Services LLC

Language: English

ASIN: B071K9RT9Z

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Amazon Best Sellers Rank:

#367,053 Paid in Kindle Store (See Top 100 Paid in Kindle Store)

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.

The examples are interesting and very easy to follow. If you have any problem applying the techniques to your data set, just a quick search would lead you to the solutions!

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!

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