no code implementations • WS 2017 • Evgeny Kim, Sebastian Pad{\'o}, Roman Klinger
Literary genres are commonly viewed as being defined in terms of content and stylistic features.
no code implementations • WS 2017 • Maximilian K{\"o}per, Evgeny Kim, Roman Klinger
Our submission to the WASSA-2017 shared task on the prediction of emotion intensity in tweets is a supervised learning method with extended lexicons of affective norms.
no code implementations • COLING 2018 • Evgeny Kim, Roman Klinger
We aim at filling this gap and present a publicly available corpus based on Project Gutenberg, REMAN (Relational EMotion ANnotation), manually annotated for spans which correspond to emotion trigger phrases and entities/events in the roles of experiencers, targets, and causes of the emotion.
no code implementations • 9 Aug 2018 • Evgeny Kim, Roman Klinger
Emotions are a crucial part of compelling narratives: literature tells us about people with goals, desires, passions, and intentions.
no code implementations • NAACL 2019 • Evgeny Kim, Roman Klinger
The development of a fictional plot is centered around characters who closely interact with each other forming dynamic social networks.
no code implementations • 6 Jun 2019 • Evgeny Kim, Roman Klinger
Our analysis shows that stories written by humans convey character emotions along various non-verbal channels.
no code implementations • WS 2019 • Evgeny Kim, Roman Klinger
Our analysis shows that stories written by humans convey character emotions along various non-verbal channels.
no code implementations • LREC 2020 • Laura Bostan, Evgeny Kim, Roman Klinger
Most research on emotion analysis from text focuses on the task of emotion classification or emotion intensity regression.
1 code implementation • LREC 2020 • Thomas Haider, Steffen Eger, Evgeny Kim, Roman Klinger, Winfried Menninghaus
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.