In this chapter, you learned what the terms KDD and data mining could mean. You have also learned about diverse ways of retrieving text from the web and even how to get a dwarf name for yourself. Otherwise, you may have learned how to run a term frequency and a clustering analysis. To wrap it up, here are the things that we did with Twitter data:

• Cleaned and transformed data
• Ran a term frequency analysis
• Drew lollipop and word cloud charts to aid interpreting
• Made hierarchical clustering from the term frequency

There is much more we could do with data retrieved from Twitter, such as the following:

• Topic modeling
• Sentiment analysis
• Follower analysis
• Retweet analysis—this might be useful for you to get more retweets
• Favorite analysis

Given that we visited some ways of retrieving and manipulating data from Twitter, I am pretty confident that you can do this by yourself now. Dig in. Write down your research notes. At some point, make a tweet, a blog post, or something like that from what you have learned and found. Publishing your learning process is a very good way to promote yourself.

I hope you liked this chapter as much as I enjoyed writing it. The next chapter is shipping you directly to the analytical land of R. You are about to witness and learn some other practical analysis made real with R, but before you proceed, I expect you to complete the quiz.