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

TL;DR

This paper evaluates ChatGPT's ability to perform affective computing tasks like personality prediction, sentiment analysis, and suicide detection, comparing it to specialized models and baselines, revealing its strengths and limitations.

Contribution

It provides the first evaluation of ChatGPT's performance on affective computing tasks, highlighting its robustness and generalist capabilities compared to specialized models.

Findings

ChatGPT performs decently on affective computing tasks.

RoBERTa outperforms ChatGPT on specific tasks.

ChatGPT is robust against noisy data.

Abstract

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.