A simple vector space model based tool for sentiment analysis of literary texts. Detailed information on: https://www.frontiersin.org/articles/10.3389/frobt.2019.00053/full and: https://www.frontiersin.org/articles/10.3389/fpsyg.2020.574746/full
SentiArt was created for non-commercial use only: if you use it please cite:
Jacobs AM (2019) Sentiment Analysis for Words and Fiction Characters From the Perspective of Computational (Neuro-)Poetics. Front. Robot. AI 6:53. doi: 10.3389/frobt.2019.00053
SentiArt has been validated with human rating data in several published studies:
Jacobs AM (2017) Quantifying the Beauty of Words: A Neurocognitive Poetics Perspective. Front. Hum. Neurosci. 11:622. doi: 10.3389/fnhum.2017.00622
Jacobs AM & Kinder A (2019). Computing the Affective-Aesthetic Potential of Literary Texts, Artifical Intelligence, 1:1, 11–27; doi:10.3390/ai1010002
Jacobs AM, Herrmann B, Lauer G, Lüdtke J and Schroeder S (2020) Sentiment Analysis of Children and Youth Literature: Is There a Pollyanna Effect? Front. Psychol. 11:574746. doi: 10.3389/fpsyg.2020.574746
The VSMs created to compute the SentiArt .xlsx tables are too big to upload on github. They can be obtained by the author via request to: sentiart0@gmail.com. The German VSM (skipgram, 300d) is based on the sdewac corpus which can be obtained at: https://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/sdewac/. The English VSM (skipgram, 500d) is based on the Gutenberg Literary English Corpus (GLEC; cf. https://www.frontiersin.org/articles/10.3389/fdigh.2018.00005/full) which can be obtained via email to the author. Both VSMs were created using the gensim library:https://github.com/RaRe-Technologies/gensim.
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