DHBeNeLux : incubator for digital humanities in Belgium, the Netherlands and Luxembourg

Digital Humanities BeNeLux is a grass roots initiative to foster knowledge networking and dissemination in digital humanities in Belgium, the Netherlands, and Luxembourg. This special issue highlights a selection of the work that was presented at the DHBenelux 2015 Conference by way of anthology for the digital humanities currently being done in the Benelux area and beyond. The introduction describes why this grass roots initiative came about and how DHBenelux is currently supporting community building and knowledge exchange for digital humanities in the Benelux area and how this is integrating regional digital humanities in the larger international digital humanities environment

Text Mining Contemporary Popular Fiction: Natural Language Processing-Derived Themes Across Over 1,000 New York Times Bestsellers and Genre Fiction Novels

This study endeavors to apply computational methods to a large dataset of popular fictional material, to see what topics emerge when viewed across genre lines and from a new, “machine” perspective. The dataset consists of 1,136 popular and commercially successful novels published between 2005 and 2016, including New York Times bestsellers and “genre fiction,” including science fiction, young adult, romance and mystery novels. Methods are discussed, including dataset preparation, LDA topic modeling and topic number optimization, qualitative topic interpretation, data analysis and visualization. The experiment was conducted in two parts, with the document or unit of analysis as each full novel, and then all of the sentences of every novel (over 9 million). 23 topics at the novel level and 66 at the sentence level were qualitatively interpreted, compared across genres and visualized. This study argues that computational tools can be generatively used to vastly broaden the scope of literary analysis, but results must still be interpreted through qualitative means. The novel may be quantitatively analyzed at both the level of the entire novel and the level of the sentence but analyzing at the level of the sentence offers more granular and interesting results. Topic modeling here identifies latent, ubiquitous topics that a human researcher may ignore or miss, re-centers research focus on the human body, its functions and the embodied nature of fiction, and was able to identify novel conventions such as linearity, characterization and settings and to distill many socially relevant topics including violence, surveillance and human institutions and activities. While topic modeling here reinforced some topical expectations based on genre conventions and tropes, topics also appeared unexpectedly in other genres: helping re-imagine the popular fiction landscape outside of genre-based siloes. Statistical analysis of a fictional dataset offers a new, birds-eye view of the contemporary popular fictional landscape, but also has many limitations, many of which are discussed

Unraveling reported dreams with text analytics

We investigate what distinguishes reported dreams from other personal narratives. The continuity hypothesis, stemming from psychological dream analysis work, states that most dreams refer to a person’s daily life and personal concerns, similar to other personal narratives such as diary entries. Differences between the two texts may reveal the linguistic markers of dream text, which could be the basis for new dream analysis work and for the automatic detection of dream descriptions. We used three text analytics methods: text classification, topic modeling, and text coherence analysis, and applied these methods to a balanced set of texts representing dreams, diary entries, and other personal stories. We observed that dream texts could be distinguished from other personal narratives nearly perfectly, mostly based on the presence of uncertainty markers and descriptions of scenes. Important markers for non-dream narratives are specific time expressions. Dream texts also exhibit a lower discourse coherence than other personal narratives

Unraveling reported dreams with text analytics

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