In this chapter, we touched upon some of the most popular social networks. You learned how to get data using Python. You understood the structure and kind of attributes data has. We explored different options provided by the API.

We explored some of the most common use cases in the context of social media mining. We touched upon the use cases about trending topics, influencer detection, information flow, and so on. We visualized some of these use cases. We also applied some of the learnings from the previous chapter, where we used NLTK to get some of the topic and entity extraction, while in scikit-learn we classified some of the complaints.

In conclusion, I would suggest that you look for some of the same use cases in context of some other social networks and try to explore them. The great part of these social networks is that all of them have a data API, and most of them are open enough to do some interesting analysis. If you apply the same learning you did in this chapter, you need to understand the API, how to get the data, and then how to apply some of the concepts we learned in the previous chapters. I hope that after learning all this, you will come up with more use cases, and some interesting analysis of social media.