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Description: Learn to use RapidMiner and One Class SVM algorithm to auto label a training set from out of sample data.
I’ve been struggling with how to separate the signal from noise in Twitter data. There’s great content and sentiment there but it’s buried by nonsense tweets and noise. How do you find that true signal within the noise?
This question wracked my brain until I solved it with a one-class SVM application in RapidMiner.
Welcome to this new tutorial on how to use RapidMiner to discover Twitter Content. I created this process as a way to monitor what’s going on in the Twitter universe and see what topics are being tweeted about. Could I do this in Python scripts? Yes, but that would be a big waste of time for me. RapidMiner makes complex ETL and tasks simple, so I live and breathe it.