Towards Emotion Classification Using Appraisal Modeling (original) (raw)

Advocating a componential appraisal model to guide emotion recognition

Most models of automatic emotion recognition use a discrete perspective and a black-box approach, i.e., they output an emotion label chosen from a limited pool of candidate terms, on the basis of purely statistical methods. Although these models are successful in emotion classification, a number of practical and theoretical drawbacks limit the range of possible applications. In this paper, the authors suggest the adoption of an appraisal perspective in modeling emotion recognition. The authors propose to use appraisals as an intermediate layer between expressive features (input) and emotion labeling (output). The model would then be made of two parts: first, expressive features would be used to estimate appraisals; second, resulting appraisals would be used to predict an emotion label. While the second part of the model has already been the object of several studies, the first is unexplored. The authors argue that this model should be built on the basis of both theoretical predictions and empirical results about the link between specific appraisals and expressive features. For this purpose, the authors suggest to use the component process model of emotion, which includes detailed predictions of efferent effects of appraisals on facial expression, voice, and body movements.

Appraisal Theories for Emotion Classification in Text

Proceedings of the 28th International Conference on Computational Linguistics

Automatic emotion categorization has been predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory, for instance following the fundamental emotion classes proposed by Paul Ekman (fear, joy, anger, disgust, sadness, surprise) or Robert Plutchik (adding trust, anticipation). This approach ignores existing psychological theories to some degree, which provide explanations regarding the perception of events. For instance, the description that somebody discovers a snake is associated with fear, based on the appraisal as being an unpleasant and non-controllable situation. This emotion reconstruction is even possible without having access to explicit reports of a subjective feeling (for instance expressing this with the words "I am afraid."). Automatic classification approaches therefore need to learn properties of events as latent variables (for instance that the uncertainty and the mental or physical effort associated with the encounter of a snake leads to fear). With this paper, we propose to make such interpretations of events explicit, following theories of cognitive appraisal of events, and show their potential for emotion classification when being encoded in classification models. Our results show that high quality appraisal dimension assignments in event descriptions lead to an improvement in the classification of discrete emotion categories. We make our corpus of appraisal-annotated emotion-associated event descriptions publicly available.

Assessing the validity of appraisal-based models of emotion

2009

Abstract We describe an empirical study comparing the accuracy of competing computational models of emotion in predicting human emotional responses in naturalistic emotion-eliciting situations. The results find clear differences in models' ability to forecast human emotional responses, and provide guidance on how to develop more accurate models of human emotion.

IJERT-Interpretation and Classification of Emotions on Computational Social Science

International Journal of Engineering Research and Technology (IJERT), 2021

https://www.ijert.org/interpretation-and-classification-of-emotions-on-computational-social-science https://www.ijert.org/research/interpretation-and-classification-of-emotions-on-computational-social-science-IJERTV10IS020276.pdf Interpretation and Classification of Emotions is perhaps one of the most popular applications of NLP. Categorization and elucidation of emotions is contextual text mining that is related with the analysis and understanding of human emotions from textual data. In the area of tourism, the internet plays a major role in the advertisement of hotels. Travelers convey their experience in the hotel by posting reviews or comments on the internet. The Vendors can be benefited by considering and correcting user reviews on the internet to improvise and assess their hotels. With the reviews available in abundance over social media , excursionists find it difficult to understand all the reviews whether they have positive or negative suggestions. It takes Computational Social Science to rapidly identify if the review is a positive or negative review. Evolution in social platforms such as tourist blogs, Twitter, Facebook and LinkedIn has fueled interest in Interpretation and Classification of Emotion. This paper focuses on extracting different categories of customer's reviews about various places of stay and analyzing the category that gives better results. Multilayer Perceptron model is used for classification of data into negative and positive sentiment categories. Classifying the data into positive and negative classes with fewer mis-classifications is the primary focus.

2005 Special Issue A systems approach to appraisal mechanisms in emotion

While artificial neural networks are regularly employed in modeling the perception of facial and vocal emotion expression as well as in automatic expression decoding by artificial agents, this approach is yet to be extended to the modeling of emotion elicitation and differentiation. In part, this may be due to the dominance of discrete and dimensional emotion models, which have not encouraged computational modeling. This situation has changed with the advent of appraisal theories of emotion and a number of attempts to develop rule-based models can be found in the literature. However, most of these models operate at a high level of conceptual abstraction and rarely include the underlying neural architecture. In this contribution, an appraisal-based emotion theory, the Component Process Model (CPM), is described that seems particularly suited to modeling with the help of artificial neural network approaches. This is due to its high degree of specificity in postulating underlying mecha...

Analysis on emotion classification methods

KDU IRC 2020, 2020

Emotional intelligence is the ability to understand changing states of emotion, it is an important aspect of human interaction. With upcoming developments emotion identification is an important aspect in HCI. Ideally if a computer can identify a human's emotions and respond to it accordingly human computer interactions would be much more natural and more convenient. But even from a human's perspective emotions are hard to identify and track, hence for a computer to identify accurate emotions can be challenging. Nonetheless there exists few methods to classify and label emotions into categories. Hence this research is an analysis of methods used to classify emotions. Discussing the strengths and weaknesses in communication cues such as facial expression classifiers, gesture movements, acoustic emotion classifiers and emotion mining in text. It argues that there exists an increment of accuracy when two or more systems are paired to extract the features in different situations. Hence results show that, while each model has its advantages and disadvantages, when integrated to classify, it gives better, more accurate prediction and improved results. Additionally, this paper mentions some of the practical issues that exist when it comes to emotion recognition and HCI. Furthermore, it is identified that emotion identification via text is a research area which holds great potential and among many approaches hand crafted models with the use of machine learning gives the best results. Finally, it proposes a solution, a mobile application for emotional support using emotion identification via text messages.

Techniques in Emotion Classi ication: An Overview

2022

Emotion classification i s a n e ssential t ask i n n atural l anguage p rocessing (NLP), a llowing systems to comprehend and respond to human emotions. This paper provides an overview of various techniques used in emotion classification, r a nging f r om t r aditional m e thods t o a d vanced Machine Learning models. I explore lexicon-based approaches, machine learning classifiers, d e ep learning methods, and the role of emojis in enhancing emotional understanding.

Context-Based Emotion Predictor: A Decision- Making Framework for Mobile Data

Mobile Information Systems

The proliferation of big data for web-enabled technologies allows users to publish their views, suggestions, sentiments, emotions, and opinionative content about several real-world entities. These available opinionative texts have greater importance to those who are inquisitive about their desired entities, but it becomes an arduous task to capture such a massive volume of user-generated content. Emotions are an inseparable part of communication, which is articulated in multiple ways and can be used for making better decisions to reshape business strategies. Emotion detection is a subdiscipline at the crossroads of text mining and information retrieval. Context is a common phenomenon in emotions and is inherently hard to capture not only for the machine but even for a human. This study proposes a decision-making framework for efficient emotion detection of mobile reviews. An unsupervised lexicon-based algorithm has been developed to tackle the problem of emotion prediction. Dictiona...

Describing Human Emotions Through Mathematical Modelling

To design a companion technology we focus on the appraisal theory model to predict emotions and determine the appropriate system behaviour to support Human-Computer-Interaction. Until now, the implementation of emotion processing was hindered by the fact that the theories needed originate from diverging research areas, hence divergent research techniques and result representations are present. Since this difficulty arises repeatedly in interdisciplinary research, we investigated the use of mathematical modelling as an unifying language to translate the coherence of appraisal theory. We found that the mathematical category theory supports the modelling of human emotions according to the appraisal theory model and hence assists the implementation.