- Taschenbuch: 122 Seiten
- Verlag: Packt Publishing (25. März 2014)
- Sprache: Englisch
- ISBN-10: 1783281774
- ISBN-13: 978-1783281770
- Größe und/oder Gewicht: 19 x 0,7 x 23,5 cm
- Durchschnittliche Kundenbewertung: Schreiben Sie die erste Bewertung
- Amazon Bestseller-Rang: Nr. 443.383 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
Social Media Mining with R (Englisch) Taschenbuch – 25. März 2014
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Über den Autor und weitere Mitwirkende
Nathan Danneman holds a PhD degree from Emory University, where he studied International Conflict. Recently, his technical areas of research have included the analysis of textual and geospatial data and the study of multivariate outlier detection. Nathan is currently a data scientist at Data Tactics, and supports programs at DARPA and the Department of Homeland Security.
Richard Heimann leads the Data Science Team at Data Tactics Corporation and is an EMC Certified Data Scientist specializing in spatial statistics, data mining, Big Data, and pattern discovery and recognition. Since 2005, Data Tactics has been a premier Big Data and analytics service provider based in Washington D.C., serving customers globally. Richard is an adjunct faculty member at the University of Maryland, Baltimore County, where he teaches spatial analysis and statistical reasoning. Additionally, he is an instructor at George Mason University, teaching human terrain analysis, and is also a selection committee member for the 20142015 AAAS Big Data and Analytics Fellowship Program. In addition to coauthoring Social Media Mining in R, Richard has also recently reviewed Making Big Data Work for Your Business for Packt Publishing, and also writes frequently on related topics for the Big Data Republic (http://www.bigdatarepublic.com/bloggers.asp#Rich_Heimann). He has recently assisted DARPA, DHS, the US Army, and the Pentagon with analytical support.
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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.
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.
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.