Social Media Mining with R
Nathan Danneman, Richard Heimann
Format: PDF / Kindle (mobi) / ePub
Deploy cuttingedge sentiment analysis techniques to realworld social media data using R
About This Book
- Learn how to face the challenges of analyzing social media data
- Get hands-on experience with the most common, up-to-date sentiment analysis tools and apply them to data collected from social media websites through a series of in-depth case studies, which includes how to mine Twitter data
- A focused guide to help you achieve practical results when interpreting social media data
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. No previous experience with R or statistics is required, though having knowledge of both will enrich your experience.
What You Will Learn
- 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
The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. However, analyzing this ever-growing pile of data is quite tricky and, if done erroneously, could lead to wrong inferences.
By using this essential guide, you will gain hands-on experience with generating insights from social media data. This book provides detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to help you accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data.
The book begins by introducing you to the topic of social media data, including its sources and properties. It then explains the basics of R programming in a straightforward, unassuming way. Thereafter, you will be made 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.
Social Media Mining in R provides a light theoretical background, comprehensive instruction, and state-of-the-art techniques, and by reading this book, you will be well equipped to embark on your own analyses of social media data.
quantitatively or in a qualitative fashion, is fundamentally about knowledge discovery and accumulation. This logic helps mitigate several shortcomings in reasoning that frequently hinder our ability to make correct inferences. Some examples include illusory correlations (perceiving correlations that do not exist), selective observation (inadvertently cherry-picking data), illogical reasoning, and over or under generalizing (assuming that facts discovered in one domain apply to others as well).
this process in detail and sums the results' set over time where the target is the US economy. Supervised social media mining – Naive Bayes classifiers Methods to extract sentiments from documents can be broadly classified into supervised and unsupervised approaches (semisupervised approaches are also available but are outside the scope of this text. Interested readers can consult Abney (2007)). Supervised methods are those that utilize data that has been tagged or labeled. In the parlance of
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introduction to social media, sentiment analysis, measurement, and inference make it appropriate for people with technical skills but little social science background. The introduction to R makes the book appropriate for people who lack any sort of programming background. The inclusion of well-studied, canonical sentiment analysis methods makes the book ideal for an introduction to this area of research, while the development of an entirely novel, unsupervised sentiment analysis model will be of
to as "letting the data speak for itself." Perhaps the strongest reason to choose quantitative methods over qualitative ones is the ability of quantitative methods, when coupled with large and valid data-sets, to generate accurate measures in the face of analyst biases. Qualitative methods, even when applied correctly, put researchers at risk of a plethora of inferential problems. Foremost is apophenia, the human tendency to discover patterns where there are none; for example, a Type I error of