Predicting customer satisfaction based on data from customer support centre

My project is aimed at predicting customer satisfaction based on the conversation they had with a customer center agent. It is possible to notice that the rating left by the customer is affected by a number of factors that can be learned from conversations. Bad ratings have common features for a whole range of such reviews, but they are different from the conversations where customers were satisfied. Since feedback is left only by a small number of people (around 15%), we will try to use machine learning algorithms to find out what ratings other – unrated tickets would have.
In this case it’s appropriate to use supervised learning – binary classification – to solve this task. I’ve decided to use this ’cause we already have some labels which are represented by 1 – negative feedback or 10 – positive feedback. Working with all the data and machine learning features, it should be possible to predict ratings for unrated tickets which can help a customer center to improve its services.