Advanced sentiment analysis for understanding affective-aesthetic responses to literary texts:A computational and experimental psychology approach to children’s literature

用于理解对文学文本的情感审美反应的高级情感分析：儿童文学的计算和实验心理学方法

• 批准号: 424250469
• 负责人: Professor Dr. Arthur M. Jacobs
• 金额: --
• 依托单位: Abteilung Buchwissenschaft
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/424250469?language=en
• 关键词: Advanced; sentiment; analysis; understanding; affective

Emotional involvement is of pivotal importance when children learn to read, tell, and share stories. This crucial dimension of cultural literacy has received surprisingly little attention within literary studies, psychology, and digital humanities. Taking a large-scale and data-driven approach, the most promising method to assess emotional information in children’s reading material is sentiment analysis. It allows the analysis of larger text corpora to find verbal emotional patterns potentially guiding young readers’ affective-aesthetic responses to literary texts – to characters, events, narrator/voice, and poem lines. Consequently, it facilitates modelling the role of emotions in the interaction of emerging literary literacy and social-cognitive development. However, standard sentiment analysis tools were developed in the (industry-driven) framework of opinion mining, do not involve concepts and theories of emotion in psychology, and need domain-adaptation to literary discourse. The main of CHYLSA is exactly what is missing: to develop and validate sentiment analysis for computational literary studies.In this follow-up proposal CHYLSA II we continue to develop advanced sentiment analysis for the use in computational literary studies in general and for children’s and youth literature in particular. In line with CHYLSA I we continue to work on corpora, further develop the advanced sentiment analysis tool ‘SentiArt’ and run cross-validation of emotion prediction by machine and by humans. 1) In contrast to CHYLSA I we stick only to corpora of children’s and youth books and texts widely read today but we do no longer include historical text no longer read today. Instead of including historical aliened text we transform selections of the already collected texts into easy-to-read versions of the same texts. We also prepare the corpora (as training sets and as database for experimental use) to be publicly available in accordance with the FAIR principle within the NFDI Text+. 2) We validate and adjust the sentiment analysis tool ‘SentiArt’ by further annotating training sets and by cross-validating in experiments on emotions in readers of this age groups. In addition to CHYLSA I we now integrate aspect-oriented transformer models to understand the relation of emotion and aspects in the development of the sentiment analysis tool. 3) We test the validity of the tool via predicting children’s reading behaviour, following the understanding of emotions by the affective neuroscience approaches by Jaak Panksepp and the fundamental distinction between valence and arousal. In addition to CHYLSA I we now include text complexity as one of the major dimensions for testing. We hope that the COVD-19 pandemic situation will turn into an endemic situation and experiments specifically with children will be easier to run than in the previous CHYLSA I project.

当孩子们学习阅读、讲述和分享故事时，情感参与是至关重要的。令人惊讶的是，文化素养的这一关键维度在文学研究、心理学和数字人文学科中几乎没有受到关注。采用大规模和数据驱动的方法，评估儿童阅读材料中的情感信息最有前途的方法是情感分析。它允许对更大的文本语料库进行分析，以找到潜在的语言情感模式，指导年轻读者对文学文本的情感-审美反应-对人物、事件、叙述者/声音和诗行的反应。因此，它有助于对情感在新兴文学素养和社会认知发展相互作用中的作用进行建模。然而，标准的情感分析工具是在(行业驱动的)意见挖掘框架下开发的，不涉及心理学中的情感概念和理论，需要领域适应文学话语。CHYLSA的主要内容正是缺失的：开发和验证计算文学研究的情感分析。在这个后续提案CHYLSA II中，我们继续开发高级情感分析，用于一般的计算文学研究，特别是儿童和青少年文学。为了与CHYLSA I保持一致，我们继续在语料库上工作，进一步开发高级情感分析工具SentiArt，并对机器和人类的情感预测进行交叉验证。1)与CHYLSA I不同，我们只关注今天广泛阅读的儿童和青少年书籍和文本的语料库，但我们不再包括今天不再阅读的历史文本。我们不包括历史异化的文本，而是将已经收集的文本的精选内容转换为相同文本的易于阅读的版本。我们还准备将语料库(作为训练集和试验性使用的数据库)根据公平原则在NFDI Text+中公开提供。2)通过对训练集的进一步标注和对该年龄段读者情绪的实验交叉验证，对情绪分析工具SentiArt进行了验证和调整。除了CHYLSA I之外，在情感分析工具的开发中，我们还集成了面向方面的转换器模型来理解情感和方面的关系。3)遵循雅克·潘克塞普的情感神经科学方法对情绪的理解，以及效价和唤醒之间的基本区别，通过预测儿童的阅读行为来检验该工具的有效性。除了CHYLSA I，我们现在还将文本复杂性作为测试的主要维度之一。我们希望，COVD-19大流行的情况将变成一种地方性情况，特别是在儿童身上进行的试验将比以前的CHYLSA I项目更容易进行。