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Individual Text Corpora Predict Openness, Interests, Knowledge and Level of Education
For training and validation, we relied on 179 participants and held out a test sample of 35 participants.
Computational analyses of the topics, sentiments, literariness, creativity and beauty of texts in a large Corpus of English Literature
We report the results of three studies providing i) topic and sentiment analyses for six text categories of GLEC (i. e., children and youth, essays, novels, plays, poems, stories) and its >100 authors, ii) novel measures of semantic complexity as indices of the literariness, creativity and book beauty of the works in GLEC (e. g., Jane Austen's six novels), and iii) two experiments on text classification and authorship recognition using novel features of semantic complexity.
Electoral Programs of German Parties 2021: A Computational Analysis Of Their Comprehensibility and Likeability Based On SentiArt
The electoral programs of six German parties issued before the parliamentary elections of 2021 are analyzed using state-of-the-art computational tools for quantitative narrative, topic and sentiment analysis.
Is Einstein more agreeable and less neurotic than Hitler? A computational exploration of the emotional and personality profiles of historical persons
Recent progress in distributed semantic models (DSM) offers new ways to estimate personality traits of both fictive and real people.
Quasi Error-free Text Classification and Authorship Recognition in a large Corpus of English Literature based on a Novel Feature Set
The Gutenberg Literary English Corpus (GLEC) provides a rich source of textual data for research in digital humanities, computational linguistics or neurocognitive poetics.
Features of word similarity
In this theoretical note we compare different types of computational models of word similarity and association in their ability to predict a set of about 900 rating data.
Explorations in an English Poetry Corpus: A Neurocognitive Poetics Perspective
This paper describes a corpus of about 3000 English literary texts with about 250 million words extracted from the Gutenberg project that span a range of genres from both fiction and non-fiction written by more than 130 authors (e. g., Darwin, Dickens, Shakespeare).
