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Found 4 papers, 1 papers with code
On Prompt Sensitivity of ChatGPT in Affective Computing
Recent studies have demonstrated the emerging capabilities of foundation models like ChatGPT in several fields, including affective computing.
A Wide Evaluation of ChatGPT on Affective Computing Tasks
In this work, we widely study the capabilities of the ChatGPT models, namely GPT-4 and GPT-3. 5, on 13 affective computing problems, namely aspect extraction, aspect polarity classification, opinion extraction, sentiment analysis, sentiment intensity ranking, emotions intensity ranking, suicide tendency detection, toxicity detection, well-being assessment, engagement measurement, personality assessment, sarcasm detection, and subjectivity detection.
Can ChatGPT's Responses Boost Traditional Natural Language Processing?
In this work, we extend this by exploring if ChatGPT has novel knowledge that would enhance existing specialised models when they are fused together.
Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPT
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).
