Chapter 4. Using Digital Humanities Computing to Study Literature

4.1 Digitization of Literary Works for Analysis
Digitization of literary works is a critical process in the realm of Digital Humanities Computing, enabling researchers to analyze and interpret texts using computational
methods. By converting physical books, manuscripts, and documents into digital formats, scholars can access, search, and analyze vast collections of literary works with ease.
One key advantage of digitizing literary works is the preservation of cultural heritage and historical texts. By creating digital copies of rare or fragile manuscripts, researchers ensure that valuable literary artifacts are safeguarded for future generations. This preservation effort also facilitates broader access to these texts, allowing scholars from around the world to study and analyze them without risking damage to the original materials.
Moreover, digitization enhances text analysis capabilities by enabling researchers to apply Natural Language Processing (NLP) techniques to large corpora of literary works. Through digitized texts, scholars can conduct sentiment analysis to uncover emotional tones within novels or poems, identify named entities like characters or locations in plays or stories through named entity recognition (NER), and explore latent themes or topics using topic modeling algorithms.
Additionally, digitized literary works support distant reading practices by providing researchers with the ability to analyze trends and patterns across extensive collections of texts. Through computational tools and techniques, scholars can uncover connections between different authors, genres, or time periods within literature, offering new insights into the evolution of literary traditions and cultural influences.

In conclusion, the digitization of literary works plays a crucial role in advancing research in Digital Humanities Computing by providing scholars with access to a wealth of textual data for analysis. By converting physical texts into digital formats and applying computational methods for text analysis, researchers can delve deeper into the study of literature, uncovering hidden patterns, themes, and relationships that contribute to a richer understanding of literary works.