Classification algorithms can be used to automatically classify documents, images, implement spam filters and in many other domains. In this tutorial we are going to use Mahout to classify tweets using the Naive Bayes Classifier. The algorithm works by using a training set which is a set of documents already associated to a category. Using this set, the classifier determines for each word, the probability that it makes a document belong to each of the considered categories. To compute the probability that a document belongs to a category, it multiplies together the individual probability of each of its word in this category. The category with the highest probability is the one the document is most likely to belong to.
To get more details on how the Naive Bayes Classifier is implemented, you can look at the mahout wiki page.
This tutorial will give you a step-by-step description on how to create a training set, train the Naive Bayes classifier and then use it to classify new tweets.
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