Java: Data Science Made Easy

Supervised learning for texts 

Supervised machine learning methods are also quite useful for text data. Like in the usual settings, here we have the label information, which we can use to understand the information within texts.

A very common example of such application of supervised learning to texts is spam detection: every time you hit the spam button in your e-mail client, this data is collected and then put in a classifier. Then, this classifier is trained to tell apart spam versus nonspam e-mails.

In this section, we will look into how to use Supervised methods for text on two examples: first, we will build a model for sentiment analysis, and then we will use a ranking classifier for reranking search results.