Abstract
In recent years, computational linguistic methods have been widely used in different literary studies where they have been proved useful in breaking into the mainstream of literary critical scholarship as well as in addressing different inherent challenges that were long associated with literary studies. Such computational approaches have revolutionized literary studies through their potentials in dealing with large datasets. They have bridged the gap between literary studies and computational and digital applications through the integration of these applications including most notably data mining in reconsidering the way literary texts are analyzed and processed. As thus, this study seeks to use the potentials of computational linguistic methods in proposing a computational model that can be usefully used in the thematic typologies of literary texts. The study adopts concept mining methods using semantic annotators for generating a thematic typology of the literary texts and exploring their thematic interrelationships through the arrangement of texts by topic. The study takes the prose fiction texts of Thomas Hardy as an example. Findings indicated that concept mining was usefully used in extracting the distinctive concepts and revealing the thematic patterns within the selected texts. These thematic patterns would be best described in these categories: class conflict, Wessex, religion, female suffering, and social realities. It can be finally concluded that computational approaches as well as scientific and empirical methodologies are useful adjuncts to literary criticism. Nevertheless, conventional literary criticism and human reasoning are also crucial and irreplaceable by computer-assisted systems.