Summary
In this chapter, we looked at text mining—how to extract features from text, how to use those features, and ways of extending those features. In doing this, we looked at putting a tweet in context—was this tweet mentioning python referring to the programming language? We downloaded data from a web-based API, getting tweets from the popular microblogging website Twitter. This gave us a dataset that we labeled using a form we built directly in the Jupyter Notebook.
We also looked at reproducibility of experiments. While Twitter doesn't allow you to send copies of your data to others, it allows you to send the tweet's IDs. Using this, we created code that saved the IDs and recreated most of the original dataset. Not all tweets were returned; some had been deleted in the time since the ID list was created and the dataset was reproduced.
We used a Naive Bayes classifier to perform our text classification. This is built upon the Bayes' theorem that uses data to update the model, unlike the...
