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.
Highlights
Emotion recognition is a vital aspect of daily human life, important for survival, social, or professional reasons
Sentiment analysis (SA) can be defined as: ‘the process of computationally identifying and categorizing opinions (According to Liu (2015) an opinion is a quintuple, ei, aij, sijkl, hk, tl, where ei is a named entity (e.g., Abraham), aij an aspect of ei, sijkl is the sentiment on aspect ai, hk is the opinion holder, and tl is the time of the opinion expressing event) expressed in a piece of text, especially in order to determine whether the writer’s attitude
The big advantage of vector space models (VSMs)-based methods like SentiArt is that they avoid these problems: (i) They require no word lists based on human ratings; (ii) thanks to the public availability of VSMs in >150 languages they can be applied to a multitude of texts from different countries even in special dialects; and (iii) by creating task- or domain-specific
Summary
Emotion recognition is a vital aspect of daily human life, important for survival, social, or professional reasons. Perhaps more than other objects of culture, written texts can induce emotions, since narratives are inseparable from the emotional content of the plots [1,2] These emotions or sentiments can determine the most ubiquitous and basic affective decision of daily life, namely deciding whether we like or dislike something/somebody [3,4]. The big advantage of VSM-based methods like SentiArt is that they avoid these problems: (i) They require no word lists based on human ratings; (ii) thanks to the public availability of VSMs in >150 languages (https://fasttext.cc/docs/en/pretrained-vectors.html) they can be applied to a multitude of texts from different countries even in special dialects; and (iii) by creating task- or domain-specific. The section describes the workflow and exact procedure of SentiArt
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