Marco Antonio Stranisci, Simona Frenda, Eleonora Ceccaldi, Valerio Basile, Rossana Damiano, Viviana Patti
| Challenge: | Existing resources for emotion recognition are lacking for appraisal models. |
| Approach: | They propose to use APPReddit to annotate non-experimental data according to Appraisal theories . they compare it with enISEAR, a corpus of events created in an experimental setting and annotated according to this theory. |
| Outcome: | The proposed model predicts four appraisal dimensions without significant loss . the proposed model is compared with enISEAR, a corpus of events created in an experimental setting and annotated for appraisal. |
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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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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. |
| Approach: | They propose that a classification setup for emotion analysis should be performed in an integrated manner, including the different semantic roles that participate in an emotion episode. |
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Task and Sentiment Adaptation for Appraisal Tagging (2023.eacl-main)
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| Challenge: | Appraisal framework in linguistics defines the framework for fine-grained evaluations and opinions. |
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RedHOT: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Media (2023.findings-eacl)
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| Challenge: | Social media platforms such as Reddit are vulnerable to misinformation and disinformation. |
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Evaluating Subjective Cognitive Appraisals of Emotions from Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing work on automatic prediction of cognitive appraisals has focused on physiological aspects of emotions. |
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An Analysis of Annotated Corpora for Emotion Classification in Text (C18-1)
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| Challenge: | Several datasets have been annotated and published for classification of emotions. |
| Approach: | They aggregated emotion corpora in a common file format with a shared annotation schema . they perform cross-corpus classification experiments to gain insight and a better understanding of differences . |
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Reproducibility and Automation of the Appraisal Taxonomy (2022.coling-1)
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| Challenge: | Existing methods for Appraisal annotation are descriptive and lack of data hinders progress . |
| Approach: | They propose to use annotated data to measure the performance of automated Appraisal annotations in a publicly available dataset. |
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EmoMent: An Emotion Annotated Mental Health Corpus from Two South Asian Countries (2022.coling-1)
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Thushari Atapattu, Mahen Herath, Charitha Elvitigala, Piyanjali de Zoysa, Kasun Gunawardana, Menasha Thilakaratne, Kasun de Zoysa, Katrina Falkner
| Challenge: | Recent research using AI and NLP demonstrates strong potential to automatically detect mental health issues from digital footprints such that professionals could provide timely interventions and mental health resources to vulnerable persons. |
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Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories (L18-1)
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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 . |
| Approach: | They use Twitter as the source of the textual data they annotate to find out which emotions often present together in tweets . |
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An Emotional Mess! Deciding on a Framework for Building a Dutch Emotion-Annotated Corpus (2020.lrec-1)
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| Challenge: | Existing frameworks for emotion recognition are limited and do not allow for categorical versus dimensional oppositions. |
| Approach: | They propose to use the emotions joy, love, anger, sadness and fear as well as dimensional models to annotate texts from different domains and topics. |
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