Summary

In this chapter, we tried to familiarize the user with the concept of social media and mining.

We discussed the OAuth API, which offers a technique for clients to allow third-party entry to their resources without sharing their credentials. It also offers a way to grant controlled access in terms of scope and duration.

We saw examples of various R packages available to visualize the text data. We discussed innovative ways to analyze and study the text data via plots. The application of sentiment analysis along with topic mining was also discussed in the same sections. To many, it's a new way to look at these kinds of data. Historically, people have used plots to plot numerical data, but plotting words on 2D graphs is very new. People have made more advances than 2D plots. With Facebook and LinkedIn, the Gephi library allows visualizing the social networks in 3D.

Next, you learned the basic steps of any data-mining problem along with various machine learning algorithms. We'll see the applications of many of these algorithms in the coming chapters. We briefly talked about sentiment analysis, anomaly detection, and various community detection algorithms. So far, we have not gone deep into any of the algorithms, but will dive into them in the later chapters.

In the next chapter, we will apply the knowledge gained so far to mine Twitter and give detailed information of the methods and techniques used there.