This chapter demonstrates how two different research traditions: computational linguistics and qualitative content analysis can be productively brought together using recent innovations in Computer Assisted Qualitative Data Analysis Software (CAQDAS) packages. It details the criteria used to evaluate available software and outlines the development of a method using NVivo to analyse the National Student Survey comments at a UK university. Automated coding of the content and sentiment, combined with human interpretation, gave insights into patterns of positive and negative student feedback related to teaching, learning and assessment. The key value of synthesising this data is in gaining insight into the processes that may drive correlations in the numeric data. Additionally, it can support identifying the atypical and exceptional practices that can have significant effect on the student experience. It concludes with notes of caution about barriers to impact in practice. The chapter is practice-focussed and extends to consider adapting the approaches for other software and surveys.