Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction

• URL: http://arxiv.org/abs/2206.05238v2
• Date: Mon, 13 Jun 2022 15:46:03 GMT
• Title: Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction
• Authors: Enrica Troiano and Laura Oberl\"ander and Roman Klinger
• Abstract summary: In psychology, the class of emotion theories known as appraisal theories aims at explaining the link between events and emotions. We analyze the suitability of appraisal theories for emotion analysis in text with the goal of understanding if appraisal concepts can reliably be reconstructed by annotators. Our comparison of text classification methods to human annotators shows that both can reliably detect emotions and appraisals with similar performance.
• Score: 14.555520007106656
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The most prominent tasks in emotion analysis are to assign emotions to texts and to understand how emotions manifest in language. An important observation for natural language processing is that emotions can be communicated implicitly by referring to events alone, appealing to an empathetic, intersubjective understanding of events, even without explicitly mentioning an emotion name. In psychology, the class of emotion theories known as appraisal theories aims at explaining the link between events and emotions. Appraisals can be formalized as variables that measure a cognitive evaluation by people living through an event that they consider relevant. They include the assessment if an event is novel, if the person considers themselves to be responsible, if it is in line with the own goals, and many others. Such appraisals explain which emotions are developed based on an event, e.g., that a novel situation can induce surprise or one with uncertain consequences could evoke fear. We analyze the suitability of appraisal theories for emotion analysis in text with the goal of understanding if appraisal concepts can reliably be reconstructed by annotators, if they can be predicted by text classifiers, and if appraisal concepts help to identify emotion categories. To achieve that, we compile a corpus by asking people to textually describe events that triggered particular emotions and to disclose their appraisals. Then, we ask readers to reconstruct emotions and appraisals from the text. This setup allows us to measure if emotions and appraisals can be recovered purely from text and provides a human baseline to judge model's performance measures. Our comparison of text classification methods to human annotators shows that both can reliably detect emotions and appraisals with similar performance. We further show that appraisal concepts improve the categorization of emotions in text.

Related papers

• Language Models (Mostly) Do Not Consider Emotion Triggers When Predicting Emotion [87.18073195745914]
  We investigate how well human-annotated emotion triggers correlate with features deemed salient in their prediction of emotions. Using EmoTrigger, we evaluate the ability of large language models to identify emotion triggers. Our analysis reveals that emotion triggers are largely not considered salient features for emotion prediction models, instead there is intricate interplay between various features and the task of emotion detection.
  arXiv  Detail & Related papers  (2023-11-16T06:20:13Z)
• Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approaches [10.736626320566707]
  The term emotion analysis in text subsumes various natural language processing tasks. We argue that emotions and events are related in two ways. We discuss how to incorporate psychological appraisal theories in NLP models to interpret events.
  arXiv  Detail & Related papers  (2023-09-05T09:56:29Z)
• Experiencer-Specific Emotion and Appraisal Prediction [13.324006587838523]
  Emotion classification in NLP assigns emotions to texts, such as sentences or paragraphs. We focus on the experiencers of events, and assign an emotion (if any holds) to each of them. Our experiencer-aware models of emotions and appraisals outperform the experiencer-agnostic baselines.
  arXiv  Detail & Related papers  (2022-10-21T16:04:27Z)
• Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection [47.60224978460442]
  We propose a Serial and Emotion-Knowledge interaction (SEEK) method for empathetic dialogue generation. We use a fine-grained encoding strategy which is more sensitive to the emotion dynamics (emotion flow) in the conversations to predict the emotion-intent characteristic of response. Besides, we design a novel framework to model the interaction between knowledge and emotion to generate more sensible response.
  arXiv  Detail & Related papers  (2022-10-21T03:51:18Z)
• Speech Synthesis with Mixed Emotions [77.05097999561298]
  We propose a novel formulation that measures the relative difference between the speech samples of different emotions. We then incorporate our formulation into a sequence-to-sequence emotional text-to-speech framework. At run-time, we control the model to produce the desired emotion mixture by manually defining an emotion attribute vector.
  arXiv  Detail & Related papers  (2022-08-11T15:45:58Z)
• x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations [13.324006587838523]
  We argue 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. Based on appraisal theories in psychology, we compile an English corpus of written event descriptions. The descriptions depict emotion-eliciting circumstances, and they contain mentions of people who responded emotionally.
  arXiv  Detail & Related papers  (2022-03-21T12:02:06Z)
• Emotion Intensity and its Control for Emotional Voice Conversion [77.05097999561298]
  Emotional voice conversion (EVC) seeks to convert the emotional state of an utterance while preserving the linguistic content and speaker identity. In this paper, we aim to explicitly characterize and control the intensity of emotion. We propose to disentangle the speaker style from linguistic content and encode the speaker style into a style embedding in a continuous space that forms the prototype of emotion embedding.
  arXiv  Detail & Related papers  (2022-01-10T02:11:25Z)
• A Circular-Structured Representation for Visual Emotion Distribution Learning [82.89776298753661]
  We propose a well-grounded circular-structured representation to utilize the prior knowledge for visual emotion distribution learning. To be specific, we first construct an Emotion Circle to unify any emotional state within it. On the proposed Emotion Circle, each emotion distribution is represented with an emotion vector, which is defined with three attributes.
  arXiv  Detail & Related papers  (2021-06-23T14:53:27Z)
• Emotion-Aware, Emotion-Agnostic, or Automatic: Corpus Creation Strategies to Obtain Cognitive Event Appraisal Annotations [12.48513712803069]
  Appraisal theories explain how the cognitive evaluation of an event leads to a particular emotion. We study different annotation strategies for appraisal dimensions based on the event-focused enISEAR corpus.
  arXiv  Detail & Related papers  (2021-02-25T13:55:44Z)
• A computational model implementing subjectivity with the 'Room Theory'. The case of detecting Emotion from Text [68.8204255655161]
  This work introduces a new method to consider subjectivity and general context dependency in text analysis. By using similarity measure between words, we are able to extract the relative relevance of the elements in the benchmark. This method could be applied to all the cases where evaluating subjectivity is relevant to understand the relative value or meaning of a text.
  arXiv  Detail & Related papers  (2020-05-12T21:26:04Z)
• Appraisal Theories for Emotion Classification in Text [13.743991035051714]
  We show that automatic classification approaches need to learn properties of events as latent variables. We propose to make such interpretations explicit, following theories of cognitive appraisal of events. Our results show that high quality appraisal dimension assignments in event descriptions lead to an improvement in the classification of discrete emotion categories.
  arXiv  Detail & Related papers  (2020-03-31T12:43:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.