You will also be introduced to solutions written in R based on R Hadoop projects. By using this essential guide, you will gain hands-on experience with generating insights from social data. A concise, handson 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. Leia nossa e nosso para obter mais detalhes. At this stage, you will be able to complete complex data mining cases and handle any issues you might encounter during projects. No previous experience with R or statistics is required, though having knowledge of both will enrich your experience. Amazing book, especially for beginners in R.
Everything was pretty easy to get setup and it than became a pretty interesting subject. Thereafter, the authors make readers aware of the inferential dangers associated with social media data, and how to avoid them, before describing and implementing a suite of social media mining techniques. Therefore I assumed that the material would be spectacular of incredibly advanced technical level. To purchase books, visit Amazon or your favorite retailer. Major books on social media mining and learning R are still necessary. Those who come across data mining problems of different complexities from web, text, numerical, political, and social media domains will find all information in this single learning path. 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 also get instructions on installing and setting up a R development environment. It includes content from the following Packt products:Learning Data Mining with R by Bater Makhabel R Data Mining Blueprints by Pradeepta Mishra Social Media Mining with R by Nathan Danneman and Richard Heimann Style and approach A complete package with which will take you from the basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining-social media mining. 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! Also the book is written by two authors with impressive careers in this field. Properly mined data forms the basis of all data analysis and computing performed on it. The emphasis in this book turned out to be more on social science than on technical matters such as machine learning algorithms. The offer was interesting however and we speculated about the content and direction of a book in a world where we had the time to take on such a task. The most likely explanation is of course that they were too busy and wanted to share the workload.
We then move on to R, installing and using it. Properly mined data forms the basis of all data analysis and computing performed on it. You will learn how to apply different mining concepts to various statistical and data applications in a wide range of fields. This lead me to believe that we were picking up the pace and I was looking forward to the next chapter. In the second chapter we finally get to see some code.
Also the book is written by two authors with impressive careers in this field. The book publisher site is. Ebook Description The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. The fourth chapter is also theoretical without any code. The author provided a good balance of theory and step-by-step approach on how to implement different social media mining techniques e. You will discover how to write code for various predication models, stream data, and time-series data. No previous experience with R or statistics is required, though having knowledge of both will enrich your experience.
Who this book is for 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. We are excited to share our experiences with you! You will learn how to manipulate data with R using code snippets and how to mine frequent patterns, association, and correlation while working with R programs. The book is written logically with a steady build up with details pertaining to social 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. 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? Everything was pretty easy to get setup and it than became a pretty interesting subject. 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. It all concludes in a pivotal chapter that provides accessible material and tangible examples, including lexicon-based, supervised, and unsupervised approaches to sentiment analysis.
Bater has been balancing a life of creativity between the edge of computer sciences and human cultures. 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. He also has experience in setting up start-ups in China. The case studies at the end are very helpful too. Properly mined data forms the basis of all data analysis and computing performed on it. We also become aware of common measurement and inference mistakes and how these failures can be avoided in applied research settings.
The tutorials in this chapter seem to be of higher level. This learning path will take you from the very basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining. The third chapter is about Twitter and obtaining Twitter data. 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. The author provided a good balance of theory and step-by-step approach on how to implement different social media mining techniques e. Packt: The book Social Media Mining with R 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. Social Media Mining with R is a concise, hands-on guide with many practical examples of mining social media data and a detailed treatise on inference and social science research that will help you in mining data in the real world.