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 –

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