I'm using Google API to categorize and predict problems with laptops (Hardware, Software, Network, Customer satisfaction, No reason). I inserted my training data and categories are very well evaluated and predicted. Training data only uses 5 main categories as it comes from free text and being specific requires some manual work.Now I want to go a level deeper and tag these categories: Example. Laptop was returned because Display was cracked:Hardware category was defined already, but I have a dictionary where I can also look for the word Display. Example:Hardware:   Display:      failure: True   Info:      Physical_damage: Display crackedUnable to install Skype:Software:   Applications:      Skype:       failure: True   Info:      Description: Unable to upgrade    I want to categorize first using main categories and after main category is chosen, look into my text for possible options from a bag of words (Example: {display: [screen, display, monitor] }, {applications, skype,office,adobe,spotify}Should I run these in 2 stages? Any recommended pattern or solution?
Data categorization
machine learning;classification;nlp;multilabel classification;nltk