Social Media Insight using Naive Bayes

Text-based documents contain lots of information. Examples include books, legal documents, social media, and e-mail. Extracting information from text-based documents is critically important to modern AI systems, for example in search engines, legal AI, and automated news services. 

Extraction of useful features from text is a difficult problem. Text is not numerical in nature, therefore a model must be used to create features that can be used with data mining algorithms. The good news is that there are some simple models that do a great job at this, including the bag-of-words model that we will use in this chapter.

In this chapter, we look at extracting features from text for use in data mining applications. The specific problem we tackle in this chapter is term disambiguation on social media - determining which meaning a word has based on its context.

We will cover...