by Bruce Smith
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 attempts to duplicate the human judgments. You could use this kind of model in your application to filter out tweet spam.
In this talk we will describe
•Some common machine learning algorithms
•Machine learning tools – free and commercial
•Acquiring and managing training data
•Extracting useful features from your documents
•Choosing the right technique for a problem
•Measuring quality and improving your model over time
•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.
11th–15th March 2011