Hi Carin, I've run some experiments with the Cortical.io classify endpoint and have found it to be a useful tool. The Python client allows you to query the endpoint [1] with positive examples that represent the class, and negative examples that do not; it may be useful to use the negative examples as those which may cause false positives. In return you get a fingerprint (bitmap) representing the class. Now you can classify text examples by first getting their fingerprints (via the term or text endpoints [2, 3]), and then calculating the distance to the category fingerprints; the API also offers an endpoint with many distance calculations [4]. So once you've created multiple category fingerprints you can classify text by determining which category is "closest", as defined by the distance metrics.
[1] https://github.com/cortical-io/python-client-sdk/blob/master/cortical/classifyApi.py [2] https://github.com/cortical-io/python-client-sdk/blob/master/cortical/termsApi.py [3] https://github.com/cortical-io/python-client-sdk/blob/master/cortical/textApi.py [4] https://github.com/cortical-io/python-client-sdk/blob/master/cortical/compareApi.py Cheers, Alex
