Evaluating social network extraction for classic and modern fiction literature

The analysis of literary works has experienced a surge in computer-assisted processing. To obtain insights into the community structures and social interactions portrayed in novels the creation of social networks from novels has gained popularity. Many methods rely on identifying named entities and relations for the construction of these networks, but many of these tools are not specifically created for the literary domain. Furthermore, many of the studies on information extraction from literature typically focus on 19th century source material. Because of this, it is unclear if these techniques are as suitable to modern-day science fiction and fantasy literature as they are to those 19th century classics. We present a study to compare classic literature to modern literature in terms of performance of natural language processing tools for the automatic extraction of social networks as well as their network structure. We find that there are no significant differences between the two sets of novels but that both are subject to a high amount of variance. Furthermore, we identify several issues that complicate named entity recognition in modern novels and we present methods to remedy these.