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Social Media Mining with R
A concise, hands-on guide with many practical examples and a detailed treatise on inference and social science research that will help you in mining data in the real world.Whether you are an undergraduate who wishes to get hands-on experience working with social data from the Web, a practitioner wishing to expand your competencies and learn unsupervised sentiment analysis, or you are simply interested in social data analysis, this book will prove to be an essential asset. No previous experience with R or statistics is required, though having knowledge of both will enrich your experience.
- GenresProgramming
122 pages, ebook
First published January 1, 2014
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Displaying 1 - 9 of 9 reviews
April 25, 2014
I decided to read “Social Media Mining with R”, because I am interested in the subject. Also the book is written by two authors with impressive careers in this field. It is unusual for a book this short (120+ pages) to be written by two experts. The most likely explanation is of course that they were too busy and wanted to share the workload. Both authors have PhDs, and as I said earlier impressive careers which also included DARPA projects. Therefore I assumed that the material would be spectacular of incredibly advanced technical level. Unfortunately I couldn’t be more wrong. The emphasis in this book turned out to be more on social science than on technical matters such as machine learning algorithms. Some chapters do not contain any code or anything of technical relevance. Therefore I feel it’s necessary to give a chapter-by-chapter overview to make it easy to skip chapters:
The first chapter is introductory, but I would barely give it a passing grade, only because of some interesting factoids that are given about the evolution of social media. This is one of those chapters that I would skip.
In the second chapter we finally get to see some code. It’s beginner level R code, which is fine by me, because I don’t know R that well. We also get instructions on installing and setting up a R development environment. If you are an experienced R coder I think you can safely skip this chapter.
The third chapter is about Twitter and obtaining Twitter data. Some of the examples here are pretty good. But again the technical details do not get enough attention in my opinion.
The fourth chapter is also theoretical without any code. Some of it seems relevant, but on the other hand it doesn’t seem that advanced or difficult.
The fifth chapter explains a bit about Naive Bayes. The model the authors give on emotions and moods, however, seems like the sort of thing any average reader can come up with on their own. I mean we all have experience with moods and emotions in our daily life. Not so much with machine learning algorithms. Don’t get me wrong I am not saying that social science is a trivial pursuit. Just that the social science material presented in this book is not exactly rocket science.
In the sixth chapter several case studies are presented with actual (finally) code. Most of the examples are US oriented, so it is assumed you know a bit about US politics and economics. The tutorials in this chapter seem to be of higher level. This lead me to believe that we were picking up the pace and I was looking forward to the next chapter. But there wasn’t a next chapter, if you don’t count the appendix that was it.
After reading this book I am left a bit disappointed, obviously because I had high (unrealistic) expectation. As I am personally not that interested in social science, I would have preferred that part to have been limited to an appendix and footnotes and I would have loved to see more code and technical background.
The first chapter is introductory, but I would barely give it a passing grade, only because of some interesting factoids that are given about the evolution of social media. This is one of those chapters that I would skip.
In the second chapter we finally get to see some code. It’s beginner level R code, which is fine by me, because I don’t know R that well. We also get instructions on installing and setting up a R development environment. If you are an experienced R coder I think you can safely skip this chapter.
The third chapter is about Twitter and obtaining Twitter data. Some of the examples here are pretty good. But again the technical details do not get enough attention in my opinion.
The fourth chapter is also theoretical without any code. Some of it seems relevant, but on the other hand it doesn’t seem that advanced or difficult.
The fifth chapter explains a bit about Naive Bayes. The model the authors give on emotions and moods, however, seems like the sort of thing any average reader can come up with on their own. I mean we all have experience with moods and emotions in our daily life. Not so much with machine learning algorithms. Don’t get me wrong I am not saying that social science is a trivial pursuit. Just that the social science material presented in this book is not exactly rocket science.
In the sixth chapter several case studies are presented with actual (finally) code. Most of the examples are US oriented, so it is assumed you know a bit about US politics and economics. The tutorials in this chapter seem to be of higher level. This lead me to believe that we were picking up the pace and I was looking forward to the next chapter. But there wasn’t a next chapter, if you don’t count the appendix that was it.
After reading this book I am left a bit disappointed, obviously because I had high (unrealistic) expectation. As I am personally not that interested in social science, I would have preferred that part to have been limited to an appendix and footnotes and I would have loved to see more code and technical background.
April 14, 2014
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 http://www.packtpub.com/social-media-....
Oh, and the book rating by me is 5 out of 5.
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 http://www.packtpub.com/social-media-....
Oh, and the book rating by me is 5 out of 5.
April 24, 2014
Review - "Social Media mining with R"
Finished reading the book.
If you have ever been interested in social media, machine learning, data science, statistical programming, or particularly Big Data — as it relates to extracting value from the data on the Web — then this book is for you. The book introduces us to the concept of social media mining, sentiment analysis, the nature of contemporary online communication, and the facets of Big Data that allow social media mining to be such a powerful tool. Additionally, it provides some evidence of the potential and pitfalls of socially generated data and argues for the use of quantitative approaches to social media mining. We then move on to R, installing and using it. It specifically lays out a technical foundation for collecting Twitter data in order to perform social data mining and provides some foundational knowledge and intuition about visualization.
We also become aware of common measurement and inference mistakes and how these failures can be avoided in applied research settings. We then move on to Social Media Mining – Fundamentals, that aims to develop theory and intuition over the models presented in the final chapter. These theoretical insights are provided prior to the step-by-step model building instructions so that researchers can be aware of the assumptions that underpin each model, and thus apply them appropriately. It all concludes in a pivotal chapter that provides accessible material and tangible examples, including lexicon-based, supervised, and unsupervised approaches to sentiment analysis.
Overall, Social Media Mining with R provides a theoretical background, comprehensive instructions, and state-of-the-art techniques such that readers will be well equipped to embark on their own analyses of social media data.
Get the book here - http://www.packtpub.com/social-media-...
Finished reading the book.
If you have ever been interested in social media, machine learning, data science, statistical programming, or particularly Big Data — as it relates to extracting value from the data on the Web — then this book is for you. The book introduces us to the concept of social media mining, sentiment analysis, the nature of contemporary online communication, and the facets of Big Data that allow social media mining to be such a powerful tool. Additionally, it provides some evidence of the potential and pitfalls of socially generated data and argues for the use of quantitative approaches to social media mining. We then move on to R, installing and using it. It specifically lays out a technical foundation for collecting Twitter data in order to perform social data mining and provides some foundational knowledge and intuition about visualization.
We also become aware of common measurement and inference mistakes and how these failures can be avoided in applied research settings. We then move on to Social Media Mining – Fundamentals, that aims to develop theory and intuition over the models presented in the final chapter. These theoretical insights are provided prior to the step-by-step model building instructions so that researchers can be aware of the assumptions that underpin each model, and thus apply them appropriately. It all concludes in a pivotal chapter that provides accessible material and tangible examples, including lexicon-based, supervised, and unsupervised approaches to sentiment analysis.
Overall, Social Media Mining with R provides a theoretical background, comprehensive instructions, and state-of-the-art techniques such that readers will be well equipped to embark on their own analyses of social media data.
Get the book here - http://www.packtpub.com/social-media-...
April 22, 2014
An informative and interesting new addition to the field of Social Media Mining, this book explains the gist of social media mining and analytics with R in a concise and easy to understand manner for the reader,from a new starter in the field of analytics to a skilled analytics worker who wants to understand and implement social media mining solutions or simply a person who just wants to know about the field .
The book is written logically with a steady build up with details pertaining to social media as a source of information and what information could be derived from it , challenges faced in the same and analytics solutions(with R language- a definite plus) have been descibed before diving into details of the solutions which have been well supplemented with diagrammatical and programmatical representions.
The best part about the book are the numerous case studies which pose a scenario/question,and the detailed logical steps and flows used in resolving and solutioning the same which provides the reader with a roadmap to develop algorithms for his future work.
Definitely recommended,its conciseness and preciseness makes it a read more like a study guide and project book than a detailed and dry textbook which is to great importance most of us with short term attention spans who do not have the patience to go through 1000 pages or more ...
The book is written logically with a steady build up with details pertaining to social media as a source of information and what information could be derived from it , challenges faced in the same and analytics solutions(with R language- a definite plus) have been descibed before diving into details of the solutions which have been well supplemented with diagrammatical and programmatical representions.
The best part about the book are the numerous case studies which pose a scenario/question,and the detailed logical steps and flows used in resolving and solutioning the same which provides the reader with a roadmap to develop algorithms for his future work.
Definitely recommended,its conciseness and preciseness makes it a read more like a study guide and project book than a detailed and dry textbook which is to great importance most of us with short term attention spans who do not have the patience to go through 1000 pages or more ...
April 13, 2014
I decided to see what Social Media Mining was all about and this book was really great at describing R and how to get started. I followed the instructions for obtaining and installing R along with using the suggested IDE. Everything was pretty easy to get setup and it than became a pretty interesting subject. There are a lot of useful things you are able to do by visually representing the data!
Following along in the book is a breeze, I really enjoyed the data mining from Twitter and how you have to be picky with the search because it is limited to 15 searches over 15 minutes!
All in all I want to say that I walked away understanding the concept that the author was relaying, I can see how mining this data could allow someone to gain insight into consumer trends and various other data related things.
If this is a field you are in I highly recommend this book.
Packt: http://www.packtpub.com/social-media-...
Following along in the book is a breeze, I really enjoyed the data mining from Twitter and how you have to be picky with the search because it is limited to 15 searches over 15 minutes!
All in all I want to say that I walked away understanding the concept that the author was relaying, I can see how mining this data could allow someone to gain insight into consumer trends and various other data related things.
If this is a field you are in I highly recommend this book.
Packt: http://www.packtpub.com/social-media-...
June 2, 2014
The book Social Media Mining with R (http://www.packtpub.com/social-media-...) is a timely text for researchers and practitioners, specially those in social sciences who want to apply the methods of social media mining and learn basic R. The author provided a good balance of theory and step-by-step approach on how to implement different social media mining techniques (e.g. lexicon-based sentiments). However, the book lacks thorough explanation on how to interpret results from the different social media mining techniques.
The book is useful as complementary material. Major books on social media mining and learning R are still necessary.
The book is useful as complementary material. Major books on social media mining and learning R are still necessary.
April 14, 2014
Amazing book, especially for beginners in R. The book provides a simple and step by step approach to data mining. Provides a lot of emphasis on the methodology to be followed while mining data. Get insight into Sentiment analysis, ways to measure and analyse data. The case studies at the end are very helpful too.
April 4, 2017
This is a good book for anyone with no previous Social Media Mining or R experience. However...the Social Media Mining part is way longer than the Source Code...which is something that could have been managed in a better way...
Still...it's a pretty good read as the explanations are very clear and to the point...
Still...it's a pretty good read as the explanations are very clear and to the point...
March 4, 2015
This book gives a nice basic introduction to R. Unfortunately, as with all code-rich books like this one, the precise (code) content ages quickly and some parts of the step-by-step examples don't work as described. You have to search a bit on the web to get through it.
Displaying 1 - 9 of 9 reviews