activity
20162025
most citedDimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction

47 citations · 239 across the 42 of their papers we have counts for

collaborators
Showing 2019 · cs.CLShow all

8 papers · 2 filters

cs.CL2019★ 41 cited

GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception

Laura Bostan, Evgeny Kim, Roman Klinger

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 tackle…

cs.CL2019

Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning

Deniz Cevher, Sebastian Zepf, Roman Klinger

The recognition of emotions by humans is a complex process which considers multiple interacting signals such as facial expressions and both prosody and semantic content of utteranc…

cs.CL2019★ 1 cited

Embedding Projection for Targeted Cross-Lingual Sentiment: Model Comparisons and a Real-World Study

Jeremy Barnes, Roman Klinger

Sentiment analysis benefits from large, hand-annotated resources in order to train and test machine learning models, which are often data hungry. While some languages, e.g., Englis…

cs.CL2019

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

Evgeny Kim, Roman Klinger

Centrality of emotion for the stories told by humans is underpinned by numerous studies in literature and psychology. The research in automatic storytelling has recently turned tow…

cs.CL2019

Crowdsourcing and Validating Event-focused Emotion Corpora for German and English

Enrica Troiano, Sebastian Padó, Roman Klinger

Sentiment analysis has a range of corpora available across multiple languages. For emotion analysis, the situation is more limited, which hinders potential research on cross-lingua…

cs.CL2019

Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis

Laura Bostan, Roman Klinger

Adjective phrases like "a little bit surprised", "completely shocked", or "not stunned at all" are not handled properly by currently published state-of-the-art emotion classificati…