6 citations · 10 across the 14 of their papers we have counts for
14 papers · 1 filter
Donate or Create? Comparing Data Collection Strategies for Emotion-labeled Multimodal Social Media Posts
Christopher Bagdon, Aidan Combs, Carina Silberer +1
Accurate modeling of subjective phenomena such as emotion expression requires data annotated with authors' intentions. Commonly such data is collected by asking study participants…
MOPO: Multi-Objective Prompt Optimization for Affective Text Generation
Yarik Menchaca Resendiz, Roman Klinger
How emotions are expressed depends on the context and domain. On X (formerly Twitter), for instance, an author might simply use the hashtag #anger, while in a news headline, emotio…
Self-Adaptive Paraphrasing and Preference Learning for Improved Claim Verifiability
Amelie Wührl, Roman Klinger
In fact-checking, structure and phrasing of claims critically influence a model's ability to predict verdicts accurately. Social media content in particular rarely serves as optima…
Entity-Level Sentiment: More than the Sum of Its Parts
Egil Rønningstad, Roman Klinger, Lilja Øvrelid +1
In sentiment analysis of longer texts, there may be a variety of topics discussed, of entities mentioned, and of sentiments expressed regarding each entity. We find a lack of studi…
"You are an expert annotator": Automatic Best-Worst-Scaling Annotations for Emotion Intensity Modeling
Christopher Bagdon, Prathamesh Karmalker, Harsha Gurulingappa +1
Labeling corpora constitutes a bottleneck to create models for new tasks or domains. Large language models mitigate the issue with automatic corpus labeling methods, particularly f…
English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts
Patrick Bareiß, Roman Klinger, Jeremy Barnes
Emotion classification in text is a challenging task due to the processes involved when interpreting a textual description of a potential emotion stimulus. In addition, the set of…