Hi! I’m trying to build a text category classifier for JIRA tickets. I found some good advice in Document classification on large articles. and split this up into two separate operations:
- Train a binary classifier to separate out text typed by humans from log files, error messages, etc.
- Train a binary classifier that determines whether the ‘human’ information output by the first model might be about a product I’m interested in.
Right now I have this as two separate models, with the output from the first being passed through the second as part of the prediction workflow. Is this the right approach, or is there some better way I can do this?