I have a dataset of community complaints and I would like to build a NLP model on those descriptions and tag a category (can be referred for an inspection or Not ie "Not referred) to each of them. Boolean answer ( Yes or No) would suffice my requirement.
For example: Our customer service department process complaints that are received via phone or email with "referred" or "not referred" status. Right now they are checking descriptions to classify them manually as "referred" or "not referred". My ultimate goal is to automate the whole and build a Machine Learning Model which gives a binary output "Yes" or No" based on descriptions. So that, they dont need to check manually and process those complaints. That ML model should categorise the future complaints into two buckets "Referred" "Not Referred" The classification of the issues they have received into buckets will help the department to provide customized solutions to the customers in each group.
Is there a way in NLP to build and train a model to automate this process? I have been reading stuffs about NLP for the past couple of days and it looks like NLP has a lot of good features to get a head start in addressing this issue. Could someone please guide me with the way I should use NLP to address this issue?
Based on recent research and recommendation, i read few article on this task. Below screenshot from one of them;
That is what I am after. We receive a bunch of messages daily from the community assisting Line and want to classify them into two buckets – of interest, not of interest.