Most approaches to emotion analysis of social media, literature, news, and other domains focus exclusively on basic emotion categories as defined by Ekman or Plutchik. However, art (such as literature) enables engagement in a broader range of more complex and subtle emotions. These have been shown to also include mixed emotional responses. We consider emotions in poetry as they are elicited in the reader, rather than what is expressed in the text or intended by the author. Thus, we conceptualize a set of aesthetic emotions that are predictive of aesthetic appreciation in the reader, and allow the annotation of multiple labels per line to capture mixed emotions within their context. We evaluate this novel setting in an annotation experiment both with carefully trained experts and via crowdsourcing. Our annotation with experts leads to an acceptable agreement of kappa = .70, resulting in a consistent dataset for future large scale analysis. Finally, we conduct first emotion classification experiments based on BERT, showing that identifying aesthetic emotions is challenging in our data, with up to .52 F1-micro on the German subset. Data and resources are available at https://github.com/tnhaider/poetry-emotion
翻译:对社交媒体、文学、新闻和其他领域进行情感分析的多数方法都完全侧重于Ekman或Plutchik所界定的基本情感类别。然而,艺术(例如文学)使得能够参与范围更广的更复杂和微妙的情感。这些都表明包括了混合的情感反应。我们认为诗歌中的情绪是在读者中产生的,而不是在文本中表达的或作者的意图中表达的。因此,我们构思了一套美感,预示读者对美感的欣赏,并允许每行多标签的注解,以捕捉其背景中的混合情绪。我们用经过仔细培训的专家和通过众包的注试验来评价这一小说设置。我们与专家的注解导致可接受的卡帕=.70的一致协议,导致未来大规模分析的数据集一致。最后,我们根据BERT进行首次情感分类实验,表明我们的数据中确定美感具有挑战性,德国子集上高达.52 F1-mimi。数据和资源可在 https://giderub/chaimle.