Recent trends in digital humanities have led to a proliferation of studies that apply ‘distant’ reading to textual data. There is an uneasy relationship between the increased use of computational methods and their application to literary studies. Much of the current literature has focused on the exploration of large corpora. However, the ability to work at this scale is often not within the power (financial or technical) or the interests, of researchers. As these large-scale studies often ignore smaller corpora, few have sought to define a clear theoretical framework within which to study small-scale text collections. In addition, while some research has been carried out on the application of term-document vector space models (topic models and frequency based analysis) to nineteenth century novels, no study exists which applies word-context models (word embeddings and semantic networks) to the novels of Austen, Edgeworth, and Owenson. This study, therefore, seeks to evaluate the use of vector space models when applied to these novels. This research first defines a theoretical framework - enhanced reading - which combines the use of close and distant reading. Using a corpus of twenty-eight nineteenth century novels as its central focus, this study also demonstrates the practical application of this theoretical approach with the additional aim of providing an insight into the authors’ representation of independence at a time of great political and social upheaval in Ireland and the UK. The use of term-document models was found to be, generally, more useful for gaining an overview of the corpora. However, the findings for word-context models reveal their ability to identify specific textual elements, some of which were not readily identified through close reading, and therefore were useful for exploring texts at both corpus and individual text level.