Applying MapReduce
In this application, we will look at predicting the gender of a writer based on their use of different words. We will use a Naive Bayes method for this, trained in MapReduce. The final model doesn't need MapReduce, although we can use the Map step to do so—that is, run the prediction model on each document in a list. This is a common Map operation for data mining in MapReduce, with the reduce step simply organizing the list of predictions so they can be tracked back to the original document.
We will be using Amazon's infrastructure to run our application, allowing us to leverage their computing resources.
Getting the data
The data we are going to use is a set of blog posts that are labeled for age, gender, industry (that is, work) and, funnily enough, star sign. This data was collected from http://blogger.com in August 2004 and has over 140 million words in more than 600,000 posts. Each blog is probably written by just one person, with some work put into verifying this (although...
