BibTeX
@InProceedings{Barth2018,
author = {Florian Barth and Evgeny Kim and Sandra Murr and Roman Klinger},
title = {A Reporting Tool for Relational Visualization and Analysis of Character Mentions in Literature},
booktitle = {Book of Abstracts -- Digital Humanities im deutschsprachigen Raum},
year = {2018},
address = {Cologne, Germany},
month = {March},
url = {https://www.romanklinger.de/publications/BarthKimMurrKlinger2018.html},
pdf = {https://www.romanklinger.de/publications/barth2018dhd.pdf}
}
@InProceedings{Bostan2020,
title = "{G}ood{N}ews{E}veryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception",
author = "Bostan, Laura Ana Maria and
Kim, Evgeny and
Klinger, Roman",
booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.194",
pages = "1554-1566",
abstract = "Most research on emotion analysis from text focuses on the task of emotion classification or emotion intensity regression. Fewer works address emotions as a phenomenon to be tackled with structured learning, which can be explained by the lack of relevant datasets. We fill this gap by releasing a dataset of 5000 English news headlines annotated via crowdsourcing with their associated emotions, the corresponding emotion experiencers and textual cues, related emotion causes and targets, as well as the reader{'}s perception of the emotion of the headline. This annotation task is comparably challenging, given the large number of classes and roles to be identified. We therefore propose a multiphase annotation procedure in which we first find relevant instances with emotional content and then annotate the more fine-grained aspects. Finally, we develop a baseline for the task of automatic prediction of semantic role structures and discuss the results. The corpus we release enables further research on emotion classification, emotion intensity prediction, emotion cause detection, and supports further qualitative studies.",
language = "English",
ISBN = "979-10-95546-34-4",
pdf = {https://www.romanklinger.de/publications/BostanKimKlinger2020LREC.pdf},
}
@InProceedings{Haider2020,
title = "{PO}-{EMO}: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in {G}erman and {E}nglish Poetry",
author = "Haider, Thomas and
Eger, Steffen and
Kim, Evgeny and
Klinger, Roman and
Menninghaus, Winfried",
booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.205",
pages = "1652-1663",
abstract = "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 k = .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.",
language = "English",
ISBN = "979-10-95546-34-4",
pdf = {https://www.romanklinger.de/publications/HaiderEgerKimKlingerMenninghaus2020LREC_PO-EMO.pdf},
}
@InProceedings{Kim2018,
author = "Kim, Evgeny
and Klinger, Roman",
title = "Who Feels What and Why? Annotation of a Literature Corpus with Semantic Roles of Emotions",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
year = "2018",
publisher = "Association for Computational Linguistics",
pages = "1345-1359",
location = "Santa Fe, New Mexico, USA",
url = "https://www.aclweb.org/anthology/C18-1114",
pdf = "https://www.aclweb.org/anthology/C18-1114.pdf",
entrysubtype={conf},
}
@ARTICLE{Kim2019a,
author = {Evgeny Kim and Roman Klinger},
title = "{A Survey on Sentiment and Emotion Analysis for Computational Literary Studies}",
journal = {Zeitschrift für Digitale Geisteswissenschaften},
year = 2019,
url = {https://arxiv.org/abs/1808.03137},
entrysubtype={journal}
}
@InProceedings{Kim2019b,
title = "An Analysis of Emotion Communication Channels in Fan-Fiction: Towards Emotional Storytelling",
author = "Kim, Evgeny and Klinger, Roman",
booktitle = "Proceedings of the Second Workshop on Storytelling",
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W19-3406",
pdf = {https://www.romanklinger.de/publications/KimKlingerStoryNLP2019ACL.pdf},
pages = "56-64",
entrysubtype={ws}
}
@InProceedings{Kim2019,
title = "Frowning {F}rodo, Wincing {L}eia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters",
author = "Kim, Evgeny and
Klinger, Roman",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/N19-1067",
pages = "647-653",
pdf = {https://www.romanklinger.de/publications/KimKlingerNAACL2019.pdf},
entrysubtype={conf}
}
@InProceedings{kim17:_inves_relat_liter_genres_emotion_plot_devel,
author = {Evgeny Kim and Sebastian Padó and Roman Klinger},
title = {Investigating the Relationship between Literary Genres and Emotional Plot Development},
booktitle = {Proceedings of the ACL LaTeCH-CLfL workshop},
year = 2017,
address = {Vancouver, BC},
keywords = {workshop myown},
url = {https://www.aclweb.org/anthology/W17-2203.pdf},
abstract = {Literary genres are commonly viewed as being defined in terms of
content and stylistic features. In this paper, we focus on one
particular class of lexical features, namely emotion
information, and investigate the hypothesis that emotion-related
information correlates with particular genres. Using genre
classification as a testbed, we compare a model that computes
lexicon-based emotion scores globally for complete stories
with a model that tracks emotion arcs through stories on a
subset of Project Gutenberg with five genres.
Our main findings are: (a), the global emotion model is competitive
with a large-vocabulary bag-of-words genre classifier (80 (b), the emotion arc model shows a lower performance (59 shows complementary behavior to the global model, as indicated by
very good performance of an oracle ensemble (94 differ in the extent to which stories follow the same emotional
arcs, with particularly uniform behavior for anger (mystery) and
fear (adventures, romance, humor, science fiction).}}
@InProceedings{Kim2017,
author = {Evgeny Kim and Sebastian Padó and Roman Klinger},
title = {{Prototypical Emotion Developments in Literary Genres}},
booktitle = {Digital Humanities 2017: Conference Abstracts},
year = {2017},
optpages = {},
address = {Montréal, Canada},
month = {August},
organization = {McGill University and Université de Montréal},
url = {https://www.romanklinger.de/publications/kim2017.pdf},
pdf = {https://dh2017.adho.org/abstracts/203/203.pdf}
}
@incollection{Klinger2020,
author = "Roman Klinger and Evgeny Kim and Sebastian Padó",
title = "Emotion Analysis for Literary Studies",
booktitle = "Reflektierte algorithmische Textanalyse",
year = "2020",
publisher = "De Gruyter",
address = "Berlin, Boston",
doi = "https://doi.org/10.1515/9783110693973-011",
pages= "237 - 268",
url = "https://www.degruyter.com/view/book/9783110693973/10.1515/9783110693973-011.xml"
}
@InProceedings{Koeper2017,
author = {Maximilian Köper and Evgeny Kim and Roman Klinger},
title = {{IMS} at {EmoInt-2017}: Emotion Intensity Prediction
with Affective Norms, Automatically Extended
Resources and Deep Learning},
booktitle = {Proceedings of the 8th Workshop on Computational
Approaches to Subjectivity, Sentiment and Social
Media Analysis},
year = {2017},
address = {Copenhagen, Denmark},
organization = {Workshop at Conference on Empirical Methods in Natural Language Processing},
publisher = {Association for Computational Linguistics},
pdf = {https://www.aclweb.org/anthology/W/W17/W17-5206.pdf},
url = {https://www.ims.uni-stuttgart.de/data/ims_emoint}
}