Win Free eCopies of Social Media Mining with R

This is a sponsored post by Richard Heimann. Rich is Chief Data Scientist at L-3 NSS and recently published Social Media Mining with R (Packt Publishing, 2014) with co-author Nathan Danneman, also a Data Scientist at L-3 NSS Data Tactics. Nathan has been featured at recent Data Science DC and DC NLP meetups.

Nathan Danneman and Richard Heimann have teamed up with DC2 to organize a giveaway of their new book, Social Media Mining with R.

Over the new two weeks five lucky winners will win a digital copy of the book. Please keep reading to find out how you can be one of the winners and learn more about Social Media Mining with R.

Overview: Social Media Mining with R

Social Media Mining with R is a concise, hands-on guide with several practical examples of social media data mining 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. Readers will learn the following:

  • Learn the basics of R and all the data types
  • Explore the vast expanse of social science research
  • Discover more about data potential, the pitfalls, and inferential gotchas
  • Gain an insight into the concepts of supervised and unsupervised learning
  • Familiarize yourself with visualization and some cognitive pitfalls
  • Delve into exploratory data analysis
  • Understand the minute details of sentiment analysis

How to Enter?

All you need to do is share your favorite effort in social media mining or more broadly in text analysis and natural language processing in the comments section of this blog. This can be some analytical output, a seminal white paper or an interesting commercial or open source package! In this way, there are no losers as we will all learn. 

The first five commenters will win a free copy of the eBook. (DC2 board members and staff are not eligible to win.) Share your public social media accounts (about.me, Twitter, LinkedIn, etc.) in your comment, or email media@datacommunitydc.org after posting.

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  • Wyatt

    The open source Tweedr package (https://github.com/dssg/tweedr/wiki) is one thing I’ve been following. It’s python based, but that shouldn’t stop anyone. I’m interested to see how disaster response and emergency management in particular, and government responsiveness and efficiency in general, will improve as more cities begin realizing the usefulness of social media.

    • Jacquelyn Bengfort

      Wyatt, thanks for your response! Please email me at media@datacommunitydc.org to claim your copy of the book.

  • Laura Lorenz

    I am amazed by word2vec (https://code.google.com/p/word2vec/), google’s vector based word association system. For example, take the word “vector” king and subtract man – now you have the word “queen”. It ties meaning to words so you can do mathematical operations on them like they are numbers.

    • Jacquelyn Bengfort

      Laura, thanks for your response! Please email me at media@datacommunitydc.org to claim your copy of the book.

    • http://socialmediaminingr.com Rich

      Great resource Laura!

  • http://calidonia.eu/ Carlos Neira Cortizas

    I would like to start applying R to mine other social networks, not only twitter. Waiting impatiently for the book.

    • Jacquelyn Bengfort

      Carlos, thanks for your response! I have provided Rich with your email address. I hope you find Social Media Mining with R to be a great resource!

  • Thomas Scherndl

    I have used the wordcloud feature in R for text analysis of conference proceedings and social media responses (tweets) about the conference. It was quite nice and easy once you get the knack of it. I took a course on Coursera which helped a lot – however I think that there is much more to discover and I have barely scratched the surface of what is possible.

    • Jacquelyn Bengfort

      Thomas, thanks for your response! I have provided Rich with your email address. I hope you find Social Media Mining with R to be a great resource!

  • Stephan Kambach

    Although it is not text-based, I really liked the approach of the Natural Capital Project (INVEST) which gathered geotagged photos in order to estimate current and future tourist visitation rates and thus recreational value throughout the wo
    rld. http://www.naturalcapitalproject.org/models/recreation.html

    • Jacquelyn Bengfort

      Stephan, thanks for your response! Please email me at media@datacommunitydc.org to claim your copy of the book.

    • http://socialmediaminingr.com Rich

      Interesting project. I tried to use the text included with pictures to classify pictures without success. Evidently the text is too sparse. In a perfect data world, these could have been used jointly.

  • Sean

    Would love to use Twitter’s search API with detailed geolocation to analyze of a certain area on how mental disorder tweets are fluctuating with time, similar to this: http://epianalysis.wordpress.com/2013/04/23/dataminementalhealth/

  • Michael Rutter

    I like to use the R package twitteR. There are many examples available on the internet on how to start using the package to access twitter data.

  • http://jejohaneman.com/ Joe

    A little late to this, but still: We Feel Fine, which was a project to collect data from a variety of blogs every ten minutes looking for the key phrases “I feel” or “I am feeling”. Lots of great data came out of it. I’m not sure if it’s still running. The web site is up, but it requires Java (it’s running an applet), and I don’t have a Java plugin anymore.

    I’m well past the fifth comment here, so I don’t expect to win, but I’m @dogboi on twitter.