To date, the evolution of archaeological knowledge production and theory has been discussed and analyzed using qualitative methods by reading vast amounts of archaeological texts in search of specific discourses or framings of the past. In this paper, we present text mining methodologies from digital humanities that can be applied to large corpora of archaeological texts to trace and evaluate changing knowledge practices. Such a big data approach is imperative. Due to the rapid increase of archaeological publications, qualitative research into the intellectual history of archaeology has become complicated and highly selective. The big data methods presented in this study were tested on a large corpus (4,811 texts totaling over 51 million words) of different types of archaeological texts from the Dutch-speaking part of Belgium. The different text mining tools were successful in identifying theoretical trends. Our tools were also successful in charting the decrease in quality due to changed organizational circumstances (developer-led archaeology). Furthermore, we could also map changing banal nationalist framings of the past.