Computing the Affective-Aesthetic Potential of Literary Texts

Abstract

In this paper, we compute the affective-aesthetic potential (AAP) of literary texts by using a simple sentiment analysis tool called SentiArt. In contrast to other established tools, SentiArt is based on publicly available vector space models (VSMs) and requires no emotional dictionary, thus making it applicable in any language for which VSMs have been made available (>150 so far) and avoiding issues of low coverage. In a first study, the AAP values of all words of a widely used lexical databank for German were computed and the VSM’s ability in representing concrete and more abstract semantic concepts was demonstrated. In a second study, SentiArt was used to predict ~2800 human word valence ratings and shown to have a high predictive accuracy (R2 > 0.5, p < 0.0001). A third study tested the validity of SentiArt in predicting emotional states over (narrative) time using human liking ratings from reading a story. Again, the predictive accuracy was highly significant: R2adj = 0.46, p < 0.0001, establishing the SentiArt tool as a promising candidate for lexical sentiment analyses at both the micro- and macrolevels, i.e., short and long literary materials. Possibilities and limitations of lexical VSM-based sentiment analyses of diverse complex literary texts are discussed in the light of these results.

Sentiment Analysis of Children and Youth Literature: Is There a Pollyanna Effect?

“…Both an English corpus from the 19 th century with only 25 different authors and a contemporary German corpus with >200 different authors clearly show a positivity bias, not only in a single text feature (as, e.g., in Dodds et al's univariate sentiment analysis), but in a variety of features such as AAP, AAP noun, happiness, or PNR. Together with those of previous cross-validation studies (e.g., Jacobs and Kinder, 2019) the results of the present Study 1 are promising, accounting for almost 70% of valence ratings. However, at the same time, they still leave ∼30% of variance unaccounted for.…”

“…The results of the cross validation shown in Figure 1 support those of the aforementioned studies, establishing a good empirical predictive validity of SentiArt for human rating data, yielding an R 2 adj = 0.68 (logistic fit; linear fit: R 2 adj = 0.65). Together with the findings of previous studies (Westbury et al, 2015;Hollis et al, 2017;Hofmann et al, 2018;Jacobs, 2019;Jacobs and Kinder, 2019) this offers even more evidence supporting the validity of VSM-based sentiment analysis tools which, in contrast to word list based tools, cannot be criticized for the aforementioned epistemological or psychometric problems. Having shown the validity of SentiArt with rating data from children of age 7 to 12, we now proceed with the computational text analyses.…”

“…Unlike most sentiment analysis tools, SentiArt computes a multivariate sentiment analysis offering a dozen affective semantic features (see Table 2 ) and is empirically validated with diverse experimental data. For example, its affective-aesthetic potential/AAP feature 4 predicted about 50% of variance in human valence ratings for >2,500 single words and about 45% of variance in “liking” ratings for entire sections from a mystery story ( Jacobs and Kinder, 2019 ). SentiArt also achieved 100% accuracy in predicting the sentiment category of 120 excerpts from the Harry Potter books ( Jacobs, 2019 ) outperforming two standard sentiment analysis tools, VADER ( Hutto and Gilbert, 2014 ) and HU-LIU ( Hu and Liu, 2004 ).…”

“…Inspired by the above citation from Boucher and Osgood (1969) and previous behavioral studies from our group also supporting the Pollyanna hypothesis (Sylvester et al, 2016), here we were interested in finding out to what extent international texts of children and youth literature (CYL) also show the Pollyanna effect, including the book who's protagonist coined the effect (Porter, 1915). For this purpose, we submitted Porter's book, as well as 372 English and 500 German books representing CYL, to a computational sentiment analysis using the empirically well validated SentiArt tool which is based on (semantic) vector space models/VSM (Jacobs, 2019;Jacobs and Kinder, 2019).…”

“…Instead of using word lists based on human valence ratings -a procedure which presents a number of both methodological and epistemological problems when trying to cross-validate the predictions of a sentiment analysis tool with other human rating data (Hollis et al, 2017;Hofmann et al, 2018) -SentiArt is based on VSMs. Using VSMs offers several advantages discussed in previous articles (Jacobs, 2019;Jacobs and Kinder, 2019), such as avoiding these problems and being applicable to any language for which VSMs are publically available (e.g., the >120 VSMs of fasttext 3 ).…”