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ChatGPT has shown the potential of emerging general artificial intelligencecapabilities, as it has demonstrated competent performance across many naturallanguage processing tasks. In this work, we evaluate the capabilities ofChatGPT to perform text classification on three affective computing problems,namely, big-five personality prediction, sentiment analysis, and suicidetendency detection. We utilise three baselines, a robust language model(RoBERTa-base), a legacy word model with pretrained embeddings (Word2Vec), anda simple bag-of-words baseline (BoW). Results show that the RoBERTa trained fora specific downstream task generally has a superior performance. On the otherhand, ChatGPT provides decent results, and is relatively comparable to theWord2Vec and BoW baselines. ChatGPT further shows robustness against noisydata, where Word2Vec models achieve worse results due to noise. Resultsindicate that ChatGPT is a good generalist model that is capable of achievinggood results across various problems without any specialised training, however,it is not as good as a specialised model for a downstream task.

Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPT