4000 stories with sentiment analysis dataset

This dataset presents 4,000 short stories that have been classified in terms of their emotional content and semantic structure. Emotional content was calculated using the valence, arousal and dominance norms in Warriner et al. (2014). Semantic structure was derived using the doc2vec algorithm, which classifies each text as a 300-place vector. The authors created this dataset as part of a study of the impact of narrative literature on mental health.

本数据集收录了4,000篇短篇小说，这些作品根据其情感内容和语义结构进行了分类。情感内容是通过Warriner等人在2014年提出的效价、唤醒和支配性规范进行计算的。语义结构则是通过doc2vec算法得出的，该算法将每一篇文本分类为一个300维度的向量。该数据集的创建是作者们研究叙事文学对心理健康影响的一部分研究项目。