Emotion Analysis from Texts (2023)

Abstract: Emotion analysis in text is an area of research that encompasses a set of various natural language processing (NLP) tasks, including classification and regression settings, as well as structured prediction tasks like role labelling or stimulus detection. In this tutorial, we provide an overview of research from emotion psychology which sets the ground for choosing adequate NLP methodology, and present existing resources and classification methods used for emotion analysis in texts. We further discuss appraisal theories and how events can be interpreted regarding their presumably caused emotion and briefly introduce emotion role labelling. In addition to these technical topics, we discuss the use cases of emotion analysis in text, their societal impact, ethical considerations, as well as the main challenges in the field.

## description and relevance automatic emotion detection in texts has been gaining popularity since 2010's @xcite . the systems for automatic emotion detection are often used on social media posts for public opinion analysis, e.g. with respect to climate change @xcite , to obtain better consumer insights @xcite , enhance prediction of corporate financial performance @xcite , or predict outcome of elections @xcite . automatic emotion detection systems are also envisioned to have an important role in building empathetic chatbots and virtual agents @xcite @xcite @xcite . more importantly, emotion analysis could be used to aid suicide prevention @xcite , and depression detection @xcite . in the computational linguistics (cl) research community, the most commonly used emotion models are ekman's model @xcite consisting of six basic emotions (anger, disgust, fear, joy, sadness, and surprise), and plutchik's model @xcite , which is commonly used focusing on eight primary emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust). however, some studies opt for different emotion frameworks or customized emotion sets. for example, @xcite , mohammad et al. (2015), demszky et al. (2020), bostan et al. (2020), and huguet cabot et al. (2021) use customized emotion sets, neviarouskaya et al. (2010) use attitudes, and troiano et al. ( 2023 ) use appraisals. since 2005, over 15 datasets manually annotated for emotions has been compiled and made freely available. the majority of datasets is in english, and they cover a variety of domains and text types: twitter data @xcite ; personal reports on emotional events @xcite ; sentences from fairy tales @xcite ; daily dialogs from websites for english language learners @xcite ; dialog utterances from the television sitcom friends @xcite ; movie subtitles @xcite ; news headlines @xcite ; and reddit comments @xcite . the xed dataset @xcite , a manually annotated dataset of movies subtitles in english and finish has been extended to 35 further languages by annotation projection to the parallel sentences in those languages. from the computational perspective, the research community has used a wide range of approaches for emotion detection and classification, e.g., traditional machine learning approaches that use emotion dictionaries @xcite , linear classifiers with various lexical, syntactic, semantic, and structural features @xcite , maximum entropy classifiers with bag-of-words as features (bostan and klinger, 2018), support vector machines and naïve bayes classifiers with various lexical, syntactic, and semantic features @xcite , cnn-based classifiers @xcite , bert-based classifiers @xcite , multitask learning @xcite , zeroshot learning @xcite , and few-shot learning @xcite . given that different architectures were tested on different domains, text types, and class types and distributions, it is not clear which models should be considered state of the art. commercial emotion analysis models commonly use either dictionary-based approaches (due to their domain customisation capabilities which do not require large amounts of labelled training data) or bert-based models (due to their domainagnostic adaptation capabilities in the case of sufficient amounts of labelled training data). since 2010's, cl research community has been exponentially increasing the effort in building models for recognising and discerning among ekman's or plutchik's basic emotions in texts (acheampong et al., 2020), and building manually annotated datasets, despite of studies in emotion psychology which suggested that detecting emotions in text is difficult and unreliable @xcite . the cl studies have pointed out several challenges in emotion annotation in texts: missing context in short utterances @xcite , non-literal meaning (mohammad, 2012), different perspectives one may take, i.e., the reader's, writer's, or text's @xcite , and high subjectivity of the task (low inter-annotator agreements were found even among trained annotators @xcite @xcite ). despite the various challenges in emotion analysis from texts, which were reported by researches in emotion psychology or natural language processing (nlp), many tools for emotion analysis are available without a thorough description of challenges and failure modes, e.g. text2emotion foot_0 and nrclex foot_1 python libraries. a large number of forprofit companies offer emotion analysis from texts, either using pre-trained models, or customised models trained on clients' data, e.g. bytesview foot_2 , komprehend foot_3 , ibm watson natural language understanding. 5 when using the paid emotion analysis apis, the identification of failure modes on specific datasets or in specific applications, the risk of unintended harms and other ethical considerations are usually shifted to the user of apis. those tasks then become extremely difficult given that companies that offer paid apis often do not disclose the model specifications and datasets the models were trained on. this tutorial has several goals. first, it provides an overview of most commonly used emotion models and their grounding in emotion psychology, their limitation and challenges from a psychological perspective as well as from nlp perspective. second, it provides an extensive overview of freely available emotion analysis datasets, their annotation strategies and limitations. third, it provides an extensive overview and critical comparison of nlp models used for emotion analysis in texts, ranging from traditional machine learning classifiers based on emotion dictionaries to transformerbased classification systems and zero-shot and fewshot learning models. finally, this tutorial aims at raising awareness about various ethical issues concerning emotion analysis and the still present challenges in emotion analysis in texts (the absence of standardized annotation and evaluation procedures, common failure modes, etc.) which need to be considered when using emotion analysis in real-world applications to avoid unintended harms. to provide the tutorial participants with a better understanding of the challenges in emotion analysis and help them get started with developing novel models for emotion analysis, we will implement (at the end of the second part of the tutorial) a small annotation exercise. ## type: cutting-edge the first part of the tutorial is an introduction to emotion psychology and the use cases of emotion analysis. the second and third part of the tutorial present cutting-edge nlp research on emotion analysis in texts. ## target audience this tutorial is well-suited for various audiences: junior and senior researches working on emotion annotation and evaluation of emotion detection models; junior and senior researches working on novel models for emotion analysis, especially those using deep-learning paradigms; industry practitioners who wish to better understand limitations of publicly available emotion analysis tools and models. there are no prerequisites for attending. however, to fully understand the discussion about strengths and limitations of different computational models, a basic knowledge of commonly used nonneural and neural classifiers is recommended. ## tutorial structure this tutorial contains three thematic parts, each to be covered in a one-hour time slot. the first part introduces emotion models, findings of relevant psychological studies, and use cases. the second part focuses on existing datasets for emotion analysis in texts, and strengths and weaknesses of the computational models which have been proposed so far. the third part covers the fine-grained emotion analysis tasks such as emotion role labeling and stimulus detection, as well as the interpretation of events with appraisal theories. in this part, we also discuss the main challenges in emotion analysis in texts, and ethical considerations for its real-world applications. although no particular prior knowledge is necessary for attending the tutorial, we recommend the attendees which are new to the emotion analysis to read the following works from the references section: ## tutorial materials all tutorial materials will be made publicly available at: eacl2023tutorial.github.io. ## ethics statement one of the main goals of the tutorial is to raise awareness about open challenges in emotion analysis which can lead to possible unintended harms and ethical issues with models commonly used for emotion analysis in real-world applications.