Search Results for author: Evgeny Kim

Found 9 papers, 1 papers with code

PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry

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.

Emotion Classification Emotion Recognition

An Analysis of Emotion Communication Channels in Fan-Fiction: Towards Emotional Storytelling

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.

An Analysis of Emotion Communication Channels in Fan Fiction: Towards Emotional Storytelling

no code implementations6 Jun 2019 Evgeny Kim, Roman Klinger

Our analysis shows that stories written by humans convey character emotions along various non-verbal channels.

A Survey on Sentiment and Emotion Analysis for Computational Literary Studies

no code implementations9 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.

Emotion Recognition Sentiment Analysis

Who Feels What and Why? Annotation of a Literature Corpus with Semantic Roles of Emotions

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.

Emotion Recognition

IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning

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.

Emotion Recognition Sentiment Analysis

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