The PEACE-Reviews dataset: Modeling Cognitive Appraisals in Emotion Text Analysis (2023.findings-emnlp)
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| Challenge: | Recent studies have delved into its significance, yet the interplay between various forms of cognitive appraisal and specific emotions, such as joy and anger, remains an area of exploration in consumption contexts. |
| Approach: | They propose to construct a dataset to model the evaluations people make about their situations based on annotated autobiographical accounts of their emotional and appraisal experiences . |
| Outcome: | The proposed model incorporates emotion, cognition, individual traits, and demographic data. |
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| Challenge: | Existing work on automatic prediction of cognitive appraisals has focused on physiological aspects of emotions. |
| Approach: | They present a dataset that assesses 24 appraisal dimensions across 241 Reddit posts . they find that open-source models fail to automatically assess and explain cognitive appraisals . |
| Outcome: | The proposed dataset assesses 24 appraisal dimensions across 241 reddit posts. |
Appraisal Theories for Emotion Classification in Text (2020.coling-main)
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| Challenge: | Automatic emotion categorization is based on textual units assigned to an emotion from a predefined inventory, for instance following the basic emotion classes proposed by Paul Ekman (1999) or Plutchik (2001). |
| Approach: | They propose to make automatic emotion categorization explicit by following theories of cognitive appraisal of events and show their potential for emotion classification when being encoded in classification models. |
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| Challenge: | Recent studies have shown that user-level features can carry more task-related information than the text itself. |
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Modeling Subjectivity in Cognitive Appraisal with Language Models (2025.findings-emnlp)
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| Challenge: | a new study explores how language models can quantify subjectivity in cognitive appraisal . existing post-hoc calibration methods fail to achieve satisfactory performance . |
| Approach: | They investigate how language models can quantify subjectivity in cognitive appraisal . existing post-hoc calibration methods often fail to achieve satisfactory performance . |
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A Comparison Of Emotion Annotation Schemes And A New Annotated Data Set (L18-1)
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| Challenge: | a series of study on positive/negative sentiments has been conducted on tweets, but recognition of more nuanced affect has received little attention . valence, arousal, dominance and surprise are the most commonly used emotion representation schemes . |
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| Challenge: | Emotion-aware Opinion Summarization (EAOS) is a framework that captures emotions that shape purchasing decisions. |
| Approach: | They propose a framework that integrates emotion into opinion summaries and a large-scale training dataset and an evaluation benchmark to support this task. |
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Guilt by Association: Emotion Intensities in Lexical Representations (2021.emnlp-main)
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| Challenge: | linguistic models have a higher correlation with human ground truth ratings than labeled data . word vectors have often been evaluated on standard word relatedness benchmarks . |
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| Challenge: | a new dataset is used to classify text into positive, negative, and neutral classes . a large amount of work on automatic detecting emotions from text has focused on classifying text into basic emotion categories . |
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x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations (2022.lrec-1)
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| Challenge: | Emotion classification is often formulated as the task to categorize texts into a predefined set of emotion classes. |
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Persona-E²: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events (2026.acl-long)
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Yuqin Yang, Haowu Zhou, Haoran Tu, Zhiwen Hui, Shiqi Yan, HaoYang Li, Dong She, Xianrong Yao, Yang Gao, Zhanpeng Jin
| Challenge: | A critical bottleneck is the lack of ground-truth human data to link personality traits to emotional shifts. |
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