Improving SVM binary classification model on new dataset I am working to create a SVM binary classifier for classification of Tweets based on news class "Crime" and "Non Crime". I have downloaded a dataset of 6400 rows from various sources and training my model on it. While I am able to achieve over 95% accuracy, what concerns me is about its performance on new datasets. The system will be deployed on live streaming tweets, so how can I put in a feedback system such as my model is continuously updates itself? As in, say for new crime type news source which were not covered in training model, how to incorporate them further?

I am asking a broad methodology question, not some specific program related question as I would like to figure out the implementation myself :)
Online learning for SVMs is supported only for certain SVM options (a linear SVM with SGD can easily update for example).  

There are implementations out there to address the issue of online learning for all SVMs.

https://stats.stackexchange.com/questions/26041/can-svm-do-stream-learning-one-example-at-a-time

If your model isn't terribly large it also probably isn't too bad to train a new one on all data periodically.