The Use of Stochastic Models in the Analysis of Vast English Literary Data Corpora

In the stylometric analysis of literary works, there are primarily specific aims, for example, at resolving the authorship, the period of the work, style, motif and purpose. These tasks, in fact, can be placed in a machine learning framework, where the quantitative features of known passages are initially learned and analyzed, and then compared with the corresponding metrics in disputable or doubtful passages. While in the past, relative laborious manual approaches relying on the human judgment of selected literary experts are mainly employed, they have shown to error-prone since human experts are unable to systematically process and methodically integrate the varied features of vast volumes of literary data. By combining a big data approach with the application of sophisticated stochastic models, more efficient, dependable and defensible decisions can be made. In this paper, we introduce two models for the quantitative analysis of the characteristics of literary work. The first is a Bernoulli model, which assumes the underlying quantitative features that are independent of each other, and the second is a Markov model, which is particularly appropriate for the analysis of poetic styles. We derive probabilistic measures of passage characteristics, and provide their illustrations in literary works in the English language. Experiments are performed the results of which corroborate the approach.