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With the rise of foundation models, a new artificial intelligence paradigmhas emerged, by simply using general purpose foundation models with promptingto solve problems instead of training a separate machine learning model foreach problem. Such models have been shown to have emergent properties ofsolving problems that they were not initially trained on. The studies for theeffectiveness of such models are still quite limited. In this work, we widelystudy the capabilities of the ChatGPT models, namely GPT-4 and GPT-3.5, on 13affective computing problems, namely aspect extraction, aspect polarityclassification, opinion extraction, sentiment analysis, sentiment intensityranking, emotions intensity ranking, suicide tendency detection, toxicitydetection, well-being assessment, engagement measurement, personalityassessment, sarcasm detection, and subjectivity detection. We introduce aframework to evaluate the ChatGPT models on regression-based problems, such asintensity ranking problems, by modelling them as pairwise rankingclassification. We compare ChatGPT against more traditional NLP methods, suchas end-to-end recurrent neural networks and transformers. The resultsdemonstrate the emergent abilities of the ChatGPT models on a wide range ofaffective computing problems, where GPT-3.5 and especially GPT-4 have shownstrong performance on many problems, particularly the ones related tosentiment, emotions, or toxicity. The ChatGPT models fell short for problemswith implicit signals, such as engagement measurement and subjectivitydetection.

A Wide Evaluation of ChatGPT on Affective Computing Tasks