1. Going Viral
In this chapter, we introduce readers to the concept of social media mining. We discuss 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, we discuss some of the potential pitfalls of socially generated data and argue for a quantitative approach to social media mining.
2. Getting Started with R
In this chapter, we lay out the case for using R for social media mining. We then walk readers through the processes of installing, getting help for, and using R. By the end of this chapter, readers will have gained familiarity with data import/export, arithmetic, vectors, basic statistical modeling, and basic graphing using R.
3. Mining Twitter with R
An obvious prerequisite to gleaning insight from social media data is obtaining the data itself. Rather than presuming readers have social media data at their disposal, we show them how to obtain and process such data. Specifically, this chapter lays a technical foundation for collecting Twitter data in order to perform social data mining.
4. Potentials and Pitfalls of Social Media Data
Socially generated data, and especially social media data, comes with many complexities. Our ability to navigate these complexities as we describe and draw inferences from this data hinges on our thinking carefully about the potentials and pitfalls that arise in social media data. This chapter highlights some of the potentials and pitfalls of social media data.
5. Social Media Mining – Fundamentals
The techniques used to extract sentiment from social media data are complex, at times counterintuitive, and often laden with assumptions. Before providing readers with a how-to guide to implement these models, we think it is critical to explain the techniques in depth so users can deploy them appropriately. This chapter explains the theoretical grounds for the techniques developed in the next chapter and serves as a bridge between the discussion of the pitfalls of social media mining and the execution of that mining.
6. Social Media Mining – Case Studies
The importance of examples cannot be downplayed as they help us to understand and subsequently to improve our skills. While this chapter represents a sizable minority of the overall book, it also represents the proportion of time spent during modeling, that is, only a sizable minority. The previous chapters have established a solid groundwork of key concepts and foundational knowledge such that readers can now responsibly digest, comprehend, and execute the case studies discussed in this chapter. This pivotal chapter provides accessible material and tangible examples, including lexicon-based, model-based, and unsupervised approaches to sentiment analysis.
7. Conclusions and Next Steps
Social media has become ubiquitous as the knowledge harnessing it is crucial to measuring the sentiments of an increasingly plugged-in population. Ignoring this information while acknowledging its presence, whether for businesses or civic purpose, constitutes an informal logical fallacy. Those businesses, politicians, social movements, and researchers who choose to ignore this data do so at their own peril and to their own detriment.