@incollection{klinger20:_emotion_analy_liter_studies,
  abstract = {Most approaches to emotion analysis in fictional texts focus on detecting the emotion class expressed over the course of a text, either with machine learning-based classification or with dictionaries. These approaches do not consider who experiences the emotion and what triggers it and therefore, as a necessary simplicifaction, aggregate across different characters and events. This constitutes a research gap, as emotions play a crucial role in the interaction between characters and the events they are involved in. We fill this gap with the development of two corpora and associated computational models which represent individual events together with their experiencers and stimuli. The first resource, REMAN (Relational EMotion ANnotation), aims at a fine-grained annotation of all these aspects on the text level. The second corpus, FANFIC, contains complete stories, annotated on the experiencer-stimulus level, i. e., focuses on emotional relations among characters. FANFIC is therefore a character relation corpus while REMAN considers event descriptions in addition. Our experiments show that computational stimuli detection is particularly challenging. Furthermore, predicting roles in joint models has the potential to perform better than separate predictions. These resources provide a starting point for future research on the recognition of emotions and associated entities in text. They support qualitative literary studies and digital humanities research. The corpora are freely available at http://www.ims.uni-stuttgart.de/data/emotion.},
  added-at = {2020-07-29T09:52:40.000+0200},
  author = {Klinger, Roman and Kim, Evgeny and Padó, Sebastian},
  biburl = {https://puma.ub.uni-stuttgart.de/bibtex/23f005472d252b1b6648b2903c2dda625/sp},
  booktitle = {Reflected Computational Text Analysis},
  editor = {Reiter, Nils and Pichler, Axel and Kuhn, Jonas},
  interhash = {0a17b8df3b26cb2fed025c4ef3168cdd},
  intrahash = {3f005472d252b1b6648b2903c2dda625},
  keywords = {myown},
  pages = {237--268},
  publisher = {De Gruyter},
  timestamp = {2020-07-29T07:55:37.000+0200},
  title = {Emotion Analysis for Literary Studies},
  url = {https://doi.org/10.1515/9783110693973-011},
  year = 2020
}