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