Many national education systems incorporate a central inspectorate tasked with visiting, evaluating and reporting on the performance of schools. The judgements produced by inspectors often play a part in the way that schools are held to account and constitute an important source of data in their own right. Inspection reports are therefore of great interest to researchers. However, the sheer quantity of inspection reports produced by national school inspectorates creates challenges for analysts. We demonstrate the use of text mining -- automated processing and analysis of unstructured textual data -- to analyse the complete corpus of school inspection reports released by the English national schools inspectorate since the turn of the century. More precisely, we report the results of a sentiment analysis, comparing the tone of inspection reports across the different grades awarded in each inspection and across different Chief Inspectors. In doing so, we hope to demonstrate the efficiency with which text mining approaches can provide representative analysis of very large volumes of inspection reports, making them a useful complement to smaller-scale, manual analyses. Resources and references are provided for researchers looking to use text mining techniques.