Sentiment Analysis for Words and Fiction Characters From the Perspective of Computational (Neuro-)Poetics
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-07-01
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| Series: | Frontiers in Robotics and AI |
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| Online Access: | https://www.frontiersin.org/article/10.3389/frobt.2019.00053/full |
| _version_ | 1828481347826483200 |
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| author | Arthur M. Jacobs Arthur M. Jacobs |
| author_facet | Arthur M. Jacobs Arthur M. Jacobs |
| author_sort | Arthur M. Jacobs |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T07:55:11Z |
| format | Article |
| id | doaj.art-175526019a1b4e158be7982ddd5874a0 |
| institution | Directory Open Access Journal |
| issn | 2296-9144 |
| language | English |
| last_indexed | 2024-12-11T07:55:11Z |
| publishDate | 2019-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Robotics and AI |
| spelling | doaj.art-175526019a1b4e158be7982ddd5874a02022-12-22T01:15:15ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442019-07-01610.3389/frobt.2019.00053441916Sentiment Analysis for Words and Fiction Characters From the Perspective of Computational (Neuro-)PoeticsArthur M. Jacobs0Arthur M. Jacobs1Department of Experimental and Neurocognitive Psychology, Freie Universität Berlin, Berlin, GermanyCenter for Cognitive Neuroscience Berlin, Berlin, GermanyTwo 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.https://www.frontiersin.org/article/10.3389/frobt.2019.00053/fullsentiment analysiscomputational poeticsemotional figure profilehybrid hero potentialmachine learningdigital humanities |
| spellingShingle | Arthur M. Jacobs Arthur M. Jacobs Sentiment Analysis for Words and Fiction Characters From the Perspective of Computational (Neuro-)Poetics Frontiers in Robotics and AI sentiment analysis computational poetics emotional figure profile hybrid hero potential machine learning digital humanities |
| title | Sentiment Analysis for Words and Fiction Characters From the Perspective of Computational (Neuro-)Poetics |
| title_full | Sentiment Analysis for Words and Fiction Characters From the Perspective of Computational (Neuro-)Poetics |
| title_fullStr | Sentiment Analysis for Words and Fiction Characters From the Perspective of Computational (Neuro-)Poetics |
| title_full_unstemmed | Sentiment Analysis for Words and Fiction Characters From the Perspective of Computational (Neuro-)Poetics |
| title_short | Sentiment Analysis for Words and Fiction Characters From the Perspective of Computational (Neuro-)Poetics |
| title_sort | sentiment analysis for words and fiction characters from the perspective of computational neuro poetics |
| topic | sentiment analysis computational poetics emotional figure profile hybrid hero potential machine learning digital humanities |
| url | https://www.frontiersin.org/article/10.3389/frobt.2019.00053/full |
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