2023/03/03 by Mostafa M. Amin, Erik Cambria, Amin, Mostafa M. +3 · 36 citations
Computer Science · Engineering · Psychology · #Artificial intelligence #Computer science #Data science #Emotion and Mood Recognition #Engineering #Machine learning #Mental Health via Writing #Natural language processing #Robustness (evolution) #Sentiment Analysis and Opinion Mining #Sentiment analysis #Task (project management) #Word2vec
paper · pdf · doi:10.48550/arxiv.2303.03186
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/03/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.