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, by Nathan Danneman Richard Heimann
Download , by Nathan Danneman Richard Heimann
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Product details
File Size: 1991 KB
Print Length: 122 pages
Publisher: Packt Publishing (March 25, 2014)
Publication Date: March 25, 2014
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B00J9B1IOI
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The book is well written but unfortunately it is based on an older version of the twitteR and OAuth packages for R and I was not able to follow the examples without extensive background research into how to authenticate and search for tweets. The Pact website did not have corrections with a solution to this basic problem as of the time of this post. Given that Twitter data is the focus of an example that takes an entire chapter, this might keep readers from following the majority of the book. For reference, instructions for authenticating twitteR are available from the author of that software here: https://github.com/geoffjentry/twitteR
The chapter that teaches you how to connect to Twitter is outdated. On section Obtaining Twitter Data, the authors The book asks to use two commands to have everything one needs to setup authorization with Twitter; install.packages('twitteR') and then trigger the library('twitteR'). When I tried, it did not work giving me OAuthFactory object error. I researched on google and got that fixed it (one needs to install two more packages install.packages(c('RCurl','ROAuth') and then load the libraries. I got through that error and now when I use next instruction, I see another error and R suggests me to use another function setup_twitter_oauth.Everything is available on the internet. I got the book thinking it will be one place where i get all what I needed but apparently it is not.I am going to try following page that has all instructions (for free!!!)https://gist.github.com/alstat/9656597
Lot of code shared here has been deprecated due to Twitter API changes. For a beginner like me, it made learning social media mining more difficult than easy!
Very good.
good book for people who want to learn mining but the material is a little old. anyway it is very good for me.
In statistical analysis, R is now the de facto standard, at least at many universities. You can readily find texts explaining how to use R for general purposes. In contrast, the current book focuses on taking R for social media mining. Chapter 2 is a too brief walkthrough of the syntax of R. I recommend if you are indeed new to R to find a lengthier text explaining R itself.Chapter 3 is where the book gets into the specifics of social data extraction. Using Twitter as a case study. Nicely at least thus far, you can get a free developer account. Don't know how long Twitter will allow this. But even if you have to pay, it may still be worthwhile. But the chapter shows how you can rapidly go some preliminary analyses, aided crucially by functions prebuilt in R.Another chapter warns of the shortcomings of trying to quantify sentiment from such sources as a Twitter data feed. There are certain features unique to Twitter. The 140 character hard limit means that writers are forced to concentrate on the message. Less verbosity than in a blog or other type of web page. The text also introduces you to the concept [and indeed the necessity] of a lexicon. And of using Bayes classifiers. The book really just touches on a vast topic. But hopefully you get the gist of how R can do this.
What an engrossing read!I must admit I fret at the beginning, how I can embark on the most advanced and seemingly difficult topics as R, Social Media, together? But somewhere early in chapter 2 I relaxed, thanks to Richard and Nathan who delivered the not so familiar (to me) content gradually and with much aplomb. I liked the R primer in same chapter.Getting Twitter data turned to be a breeze as you will see in chapter 3. Chapters 4 and 5 are not exactly technical, for example they expand on the nature of sentiments, social behaviour, and mentioned a few pitfalls, however to my surprise, I enjoyed them a lot. Most importantly, these two chapters serve as a base to the rest of the book in terms of a model on which the analysis is going to be conducted. Chapter 6 is where you work hard, but not too hard than it would make you put the book away and shut your computer down, rather it was fun full of algorithms, graphics and cool insight!I finished this book in no time, but wish it was longer. Certainly, the authors are the ones I will be looking for to buy more books from.Like I said, this is a somewhat a short book, but it covers what it promises very well, for those who wish to expand further the authors provide a list of related literature.I think a contractor wishing to deliver a social analysis assignment fast should not look any further. And one can sure expand further than extracting tweets. I trust the principals and techniques remain almost the same.In terms of my closing notes, the reader needs to be familiar with Git[Hub], some or no R and better running a 64 bit OS, preferably Linux or Mac (mainly because these OSes already come with tools as CURL). The book publisher site is [...]....Oh, and the book rating by me is 5 out of 5.
At first, I thought it was just another book about social media with a new view using R instead of other softwares/programming languages. But I was wrong in some ways.The book is well written and along the book the authors remind us the facts about social media. Everything is explained ! The context is well presented and they convince about the necessity of social media analysis. I really liked the chapter dedicated to cases studies, once all methodologies are presented - from preprocessing up to the modelling task itself.But there is a lack of mathematical rigour on the book. Just one equation is presented along the book. Also, I think the book is too short (there is at least three subjects: social media context, statistical methodologies and the use of R software).If you are a beginner in this area, I really recommend this book.
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