Front Artif Intell. 2022 Feb 22;5:718690. doi: 10.3389/frai.2022.718690. eCollection 2022.

ABSTRACT

Recent progress in machine-learning-based distributed semantic models (DSMs) offers new ways to simulate the apperceptive mass (AM; Kintsch, 1980) of reader groups or individual readers and to predict their performance in reading-related tasks. The AM integrates the mental lexicon with world knowledge, as for example, acquired via reading books. Following pioneering work by Denhière and Lemaire (2004), here, we computed DSMs based on a representative corpus of German children and youth literature (Jacobs et al., 2020) as null models of the part of the AM that represents distributional semantic input, for readers of different reading ages (grades 1-2, 3-4, and 5-6). After a series of DSM quality tests, we evaluated the performance of these models quantitatively in various tasks to simulate the different reader groups' hypothetical semantic and syntactic skills. In a final study, we compared the models' performance with that of human adult and children readers in two rating tasks. Overall, the results show that with increasing reading age performance in practically all tasks becomes better. The approach taken in these studies reveals the limits of DSMs for simulating human AM and their potential for applications in scientific studies of literature, research in education, or developmental science.

PMID:35280232 | PMC:PMC8905622 | DOI:10.3389/frai.2022.718690

Front Robot AI. 2019 Jul 17;6:53. doi: 10.3389/frobt.2019.00053. eCollection 2019.

ABSTRACT

Two computational studies provide different sentiment analyses for text segments (e.g., "fearful" passages) and figures (e.g., "Voldemort") from the Harry Potter books (Rowling, 1997, 1998, 1999, 2000, 2003, 2005, 2007) based on a novel simple tool called SentiArt. The tool uses vector space models together with theory-guided, empirically validated label lists to compute the valence of each word in a text by locating its position in a 2d emotion potential space spanned by the words of the vector space model. After testing the tool's accuracy with empirical data from a neurocognitive poetics study, it was applied to compute emotional figure and personality profiles (inspired by the so-called "big five" personality theory) for main characters from the book series. The results of comparative analyses using different machine-learning classifiers (e.g., AdaBoost, Neural Net) show that SentiArt performs very well in predicting the emotion potential of text passages. It also produces plausible predictions regarding the emotional and personality profile of fiction characters which are correctly identified on the basis of eight character features, and it achieves a good cross-validation accuracy in classifying 100 figures into "good" vs. "bad" ones. The results are discussed with regard to potential applications of SentiArt in digital literary, applied reading and neurocognitive poetics studies such as the quantification of the hybrid hero potential of figures.

PMID:33501068 | PMC:PMC7805775 | DOI:10.3389/frobt.2019.00053

Front Psychol. 2020 Sep 24;11:574746. doi: 10.3389/fpsyg.2020.574746. eCollection 2020.

ABSTRACT

If the words of natural human language possess a universal positivity bias, as assumed by Boucher and Osgood's (1969) famous Pollyanna hypothesis and computationally confirmed for large text corpora in several languages (Dodds et al., 2015), then children and youth literature (CYL) should also show a Pollyanna effect. Here we tested this prediction applying an unsupervised vector space model-based sentiment analysis tool called SentiArt (Jacobs, 2019) to two CYL corpora, one in English (372 books) and one in German (500 books). Pitching our analysis at the sentence level, and assessing semantic as well as lexico-grammatical information, both corpora show the Pollyanna effect and thus add further evidence to the universality hypothesis. The results of our multivariate sentiment analyses provide interesting testable predictions for future scientific studies of literature.

PMID:33071913 | PMC:PMC7541694 | DOI:10.3389/fpsyg.2020.574746

Front Hum Neurosci. 2018 Jan 30;12:12. doi: 10.3389/fnhum.2018.00012. eCollection 2018.

ABSTRACT

[This corrects the article on p. 622 in vol. 11, PMID: 29311877.].

PMID:29406540 | PMC:PMC5797657 | DOI:10.3389/fnhum.2018.00012

Front Hum Neurosci. 2017 Dec 19;11:622. doi: 10.3389/fnhum.2017.00622. eCollection 2017.

ABSTRACT

In this paper I would like to pave the ground for future studies in Computational Stylistics and (Neuro-)Cognitive Poetics by describing procedures for predicting the subjective beauty of words. A set of eight tentative word features is computed via Quantitative Narrative Analysis (QNA) and a novel metric for quantifying word beauty, the aesthetic potential is proposed. Application of machine learning algorithms fed with this QNA data shows that a classifier of the decision tree family excellently learns to split words into beautiful vs. ugly ones. The results shed light on surface and semantic features theoretically relevant for affective-aesthetic processes in literary reading and generate quantitative predictions for neuroaesthetic studies of verbal materials.

PMID:29311877 | PMC:PMC5742167 | DOI:10.3389/fnhum.2017.00622