Using the Mahout Naive Bayes Classifier to automatically classify Twitter messages (part 2: distribute classification with hadoop)

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In this post, we are going to categorize the tweets by distributing the classification on the hadoop cluster. It can make the classification faster if there is a huge number of tweets to classify.

To go through this tutorial you would need to have run the commands in the post Using the Mahout Naive Bayes Classifier to automatically classify Twitter messages.

To distribute the classification on the hadoop nodes, we are going to define a mapreduce job:

  • the csv containing the tweets to classify is split into several chunks
  • each chunk is sent to the hadoop node that will process it by running the map class
  • the map class loads the naive bayes model and some document/word frequency into memory
  • for each tweet of the chunk, it computes the best matching category. The result is written in the output file. We are not using a reducer class as we don’t need to do aggregations.

To download the code used in this post, you can fetch it from github:

$ git clone https://github.com/fredang/mahout-naive-bayes-example2.git

To compile the project:

$ mvn clean package assembly:single

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Using the Mahout Naive Bayes Classifier to automatically classify Twitter messages

mahout2Classification 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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