Appraisal-based Emotion Analysis

Corpus

Jan Hofmann, Enrica Troiano, Kai Sassenberg, Laura Oberländer, Maximilian Wegge, Roman Klinger

Corpora and Models for Appraisal Classification for Emotion Analysis. We explain the relation between these corpora in a dedicated blog post.

Jan Hofmann, Enrica Troiano, Kai Sassenberg, and Roman Klinger. Appraisal Theories for Emotion Classification in Text. Proceedings of the 28th International Conference on Computational Linguistics (COLING). 2020. https://aclanthology.org/2020.coling-main.11/

Jan Hofmann, Enrica Troiano, and Roman Klinger. Emotion-aware, emotion-agnostic, or automatic: A study of manual annotation strategies for cognitive event appraisal and the influence on model performance. In Proceedings of the 11th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 2021. https://aclanthology.org/2021.wassa-1.17.pdf

Enrica Troiano, Laura Oberländer, Maximilian Wegge, and Roman Klinger.  A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations. LREC 2022. https://aclanthology.org/2022.lrec-1.146/

Maximilian Wegge, Enrica Troiano, Laura Oberländer, and Roman Klinger. Experiencer-specific emotion and appraisal prediction. In Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science, Abu Dhabi, United Arab Emirates and online, December 2022. Association for Computational Linguistics. https://www.romanklinger.de/publications/WeggeTroianoOberlaenderKlinger.pdf

Enrica Troiano, Laura Oberländer, and Roman Klinger. Appraisal Theories for Dimensional Modelling of Emotions in Text. submitted to the Computational Linguistics Journal. 2023. http://dx.doi.org/10.1162/coli_a_00461

• The original data published in the COLING paper is available at https://www.romanklinger.de/data-sets/appraisalEnISEAR.zip. Code is available at https://github.com/bluzukk/appraisal-emotion-classification/. In this paper, we reannotated the enISEAR with seven appraisal dimensions from the perspective of the original author.
• The data including the results of the annotation experiments from the WASSA paper can be found at https://www.romanklinger.de/data-sets/HofmannTroianoKlinger-Appraisal-Data.zip. The goal of this work has been to evaluate if appraisals can also be automatically assigned based on the existing emotion categories.
• The data from the LREC paper is available at https://www.romanklinger.de/data-sets/x-enVENT.zip. These data is also a reannotation of the enISEAR data (+ some other resources, but only few instances). The difference to the COLING paper is two-fold: We have ~20 appraisal dimensions and we annotated experiencer-specificly – multiple people who participate in an event get each their own annotation.
• We performed experiments with experiencer-specific modelling in the NLPCSS paper. The code of these experiments is available at https://bitbucket.org/weggem/experiencer-specific-emotion-and-appraisal-prediction
• The corpus crowd-enVENT (described in the CL Journal article) is a corpus of event reports that describe events that caused particular emotions. Each author of an event description also reports their own appraisal, according to 21 variables. You can find this data, including a more detailed description, at https://www.romanklinger.de/data-sets/crowd-enVent2022.zip. The code for the experiments is in https://github.com/sarnthil/crowd-enVent-modeling.