For more information, you can refer to the following books:
- Taming Text: How to Find, Organize, and Manipulate It, Ingersoll, Morton, and Farris: This is a pragmatic volume that covers exactly what its title promises. As data scientists, it has been noted that we spend around 80 percent of our time arranging our data for analysis. This book can help to trim that figure, allowing you to spend more time analyzing and interpreting, rather than data munging.
- Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More, Matthew Russell: This is an excellent resource for practitioners wanting to learn how to get their hands on a wide array of social and web data. The book also features a companion piece that deals with analyzing social data. Readers should note that this pair of books utilizes Python rather than R.
- Speech and Language Processing, Jurafski and Martin: This book provides a slightly more advanced and fairly wide review of many techniques and technologies applied to language. This book is slightly more technical than Social Media Mining in R; it is likely inappropriate for those unfamiliar with optimization.
- The Handbook of Computational Linguistics and Natural Language Processing, Clark, Fox, and Lappin: This is another fairly broad book that covers many NLP topics, but with more of a focus on machine learning than theoretical statistics. It is suitable for graduate-level students and researchers.
- Machine Learning with R, Brett Lanz: This is a nice volume that covers a wide array of machine-learning algorithms. While not aimed specifically at social media, readers will find several of the techniques applicable.
- Finding Groups in Data: An Introduction to Cluster Analysis, Kaufman and Rousseeuw: This is a well-crafted book that makes an excellent first read on clustering techniques, which we mentioned in Chapter 3, Mining Twitter with R. The book covers several clustering techniques in an intuitive and non-technical manner.
- A First Course in Statistical Programming with R, Braun and Murdoch: This is an excellent resource for the world's most popular and fastest-growing statistical programming language. This book goes into great detail about programming constructs and graphical parameters, all with an eye towards building a student's competencies in applied statistics.
- Designing Social Research, Normal Blaikie: This is an excellent first book on research design. It carefully walks readers through knowledge areas such as theory testing, measurement, and inference.
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