问题
My machine learning goal is to search for potential risks (will cost more money) and opportunities (will save money) from a Project Requirements document.
My idea is to classify sentences from the data into one of these categories: Risk, Opportunity and Irrelevant (no risk, no opportunity, default categorie).
I will use a multinomial Bayes classifier for this with tf-dif.
Now I need to have data for my training set and test set. The way I will do this is label every sentence from requirement documents with 1 of the 3 categories. Is this a good approach?
Or should I only label sentences which are obviously a risk/opportunity/irrelevant?
Also, is the Irrelevant categorie a good idea?
回答1:
I believe the three-class approach is a good one. This is similar to sentiment analysis, where you typically have positive, negative and neutral documents (or sentences). The neutral comprises the vast majority of the instances, so your classification problem will be unbalanced. That is not necessarily an issue, but for difficult problems like this one, a naive bayes classifier might simply classify everything in the neutral/irrelevant bucket since the prior for neutral will be quite high.
- your sampling (labeling) should be representative of the reality. Don't try to create a dataset of 1000 risk, 1000 opportunity, 1000 irrelevant. Instead, take a sample of say 10000 requirements, and assign the proper label to each, even if it means having much more 'Irrelevant' than 'Risk' for instance.
- text classification models require many instances, since the search space is vast. I wonder if you have considered the fact that to get reliable results (say over 90%), you may need to manually label thousands of instances.
- and even if you have thousands of training instances, your problem looks particularly difficult, unless there are some obvious keywords to trigger 'risk' or 'opportunity' that I don't understand. Ask yourself: would this be easy for a human to judge? If you asked 3 judges to classify your requirements, would they all come up with the same answer? If not, then it might be 10s of thousands of training documents that you will need, and the classification accuracy may still be disappointing.
来源：https://stackoverflow.com/questions/39444786/how-to-create-training-data-for-text-classification-on-4-categories