Papers with labeling approach

2 papers
Integrating Plutchik’s Theory with Mixture of Experts for Enhancing Emotion Classification (2024.emnlp-main)

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Challenge: Existing methods for emotion classification ignore the sentimental aspects of text, resulting in a lack of generalization and sampling bias.
Approach: They propose a method for emotion classification using Plutchik’s Wheel of Emotions theory and a Mixture of Experts architecture to evaluate the effectiveness.
Outcome: The proposed method improves the performance of emotion classification.
PolitiSky24: U.S. Political Bluesky Dataset with User Stance Labels (2025.findings-emnlp)

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Challenge: Stance detection is a method of identifying the viewpoint expressed in text toward a specific target, such as a political figure.
Approach: They present a dataset for the 2024 U.S. presidential election that includes 16,044 user-target stance pairs enriched with engagement metadata, interaction graphs, and user posting histories.
Outcome: The proposed dataset comprises 16,044 user-target stance pairs enriched with engagement metadata, interaction graphs, and user posting histories.

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