Social media applications encounter messy user-generated data in blog posts, status updates, tweets, user profiles, etc. These documents contain free-form text that obeys no particular rules of grammar, punctuation or spelling. If the data is so messy, how can a computer program recognize adult content or hate speech or spam? How can a computer program tell the difference between an advertisement... [read more]

Social media applications encounter messy user-generated data in blog posts, status updates, tweets, user profiles, etc. These documents contain free-form text that obeys no particular rules of grammar, punctuation or spelling. If the data is so messy, how can a computer program recognize adult content or hate speech or spam? How can a computer program tell the difference between an advertisement and a product review? How can a computer program distinguish between a positive and a negative product review? Machine learning offers some solutions. For example, given sample tweets labeled (by people) as spam or non-spam, machine learning tools can generate a program (or model) that makes similar judgments. You could use this in your application to filter out tweet spam. I will describe some machine learning tools, how to acquire, label and manage training data and how to extract features from your documents. I will also talk about choosing the right technique for a problem, measuring quality and improving your model over time, and integrating a machine learned model with your application. Coming out of this session, you will know where you might use machine learning in your applications, and you will know how to get started.