The CLS INFRA Survey of Methods in Computational Literary Studies

This resource offers an introduction to several research areas and issues that are prominent within Computational Literary Studies (CLS) as well as to several key methodological concerns that are of importance when performing research in CLS.

This publication from the CLS INFRA project documents and describes current, widespread research practices in CLS, based on a large collection of publications that have been published in this field over the last approximately ten years. 

The basis of this is the Survey of Methods in Computational Literary Studies. The perspective of this survey is primarily descriptive: it aims to document current, widespread practices as the authors were able to observe them in the published literature. In this sense, the survey, while far from exhaustive, can also serve as an annotated bibliography of sorts and as a guide to further reading.

The survey covers the following key concerns in CLS: authorship attribution, literary history, literary genre, gender in literature, and canonicity/prestige. And for each of these concerns, it offers sections on the following key steps in the research process: corpus building, preprocessing and annotation, data analysis, and evaluation.

The survey is complemented by four showcases, or case studies, of research in CLS, each focusing on a different literary genre: one on stylometric authorship attribution using novels, one one network analysis in dramatic texts, one on metric analysis in poetry and one on the uses of linked open data for correspondences and other literary texts more generally. The showcases are available at https://showcases.clsinfra.io.

The survey chapters and showcases can be consulted selectively and read in various orders, resulting in an individualised learning process.

CLS INFRA has received funding from the European Union’s Horizon 2020 Research and Innovation Programme.

#Learning Outcomes

After reviewing this resource, learners will:

• obtain a solid understanding of key concerns and key steps of the research process in Computational Literary Studies (CLS).
• be able to identify best practices as well as further readings relevant to research in CLS.
• gain a deeper understanding of issues related to specific methods of analysis as applied to specific literary corpora.