# A Wide Evaluation of ChatGPT on Affective Computing Tasks

arXiv: https://arxiv.org/abs/2308.13911 · HTML: https://mlsanity.com/paper/2308.13911/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 70 references that are in the map.
- First posted on arXiv 26 Aug 2023.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023)
- [A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT](https://mlsanity.com/paper/2302.09419/) (2023)
- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [Sentiment Analysis in the Era of Large Language Models: A Reality Check](https://mlsanity.com/paper/2305.15005/) (2023)
- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [ChatGPT: Jack of all trades, master of none](https://mlsanity.com/paper/2302.10724/) (2023)
- [Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT](https://mlsanity.com/paper/2302.10198/) (2023)
- [Is ChatGPT a Good Sentiment Analyzer? A Preliminary Study](https://mlsanity.com/paper/2304.04339/) (2023)
- [GPTEval: A Survey on Assessments of ChatGPT and GPT-4](https://mlsanity.com/paper/2308.12488/) (2023)
- [Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPT](https://mlsanity.com/paper/2303.03186/) (2023)
- [WASSA-2017 Shared Task on Emotion Intensity](https://mlsanity.com/paper/1708.03700/) (2017)
- [Is ChatGPT a Good Personality Recognizer? A Preliminary Study](https://mlsanity.com/paper/2307.03952/) (2023)
- [Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings](https://mlsanity.com/paper/2209.07494/) (2022)
- [Can ChatGPT's Responses Boost Traditional Natural Language Processing?](https://mlsanity.com/paper/2307.04648/) (2023)

## Built on by (most cited first, 30 of 30)

- [A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics](https://mlsanity.com/paper/2310.05694/) (2023)
- [EmoBench: Evaluating the Emotional Intelligence of Large Language Models](https://mlsanity.com/paper/2402.12071/) (2024)
- [Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective](https://mlsanity.com/paper/2408.04638/) (2024)
- [Theory of Mind in Large Language Models: Assessment and Enhancement](https://mlsanity.com/paper/2505.00026/) (2025)
- [A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends](https://mlsanity.com/paper/2311.10777/) (2023)
- [MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis](https://mlsanity.com/paper/2411.11235/) (2024)
- [On Prompt Sensitivity of ChatGPT in Affective Computing](https://mlsanity.com/paper/2403.14006/) (2024)
- [Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation](https://mlsanity.com/paper/2404.02589/) (2024)
- [Affective Computing Has Changed: The Foundation Model Disruption](https://mlsanity.com/paper/2409.08907/) (2024)
- [Modeling Uncertainty in Personalized Emotion Prediction with Normalizing Flows](https://mlsanity.com/paper/2312.06034/) (2023)
- [A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2412.02279/) (2024)
- [Explainable Sentiment Analysis with DeepSeek-R1: Performance, Efficiency, and Few-Shot Learning](https://mlsanity.com/paper/2503.11655/) (2025)
- [Emotions in the Loop: A Survey of Affective Computing for Emotional Support](https://mlsanity.com/paper/2505.01542/) (2025)
- [Recognizing Emotion Regulation Strategies from Human Behavior with Large Language Models](https://mlsanity.com/paper/2408.04420/) (2024)
- [Can Large Language Models Capture Video Game Engagement?](https://mlsanity.com/paper/2502.04379/) (2025)
- [EmoAgent: A Multi-Agent Framework for Diverse Affective Image Manipulation](https://mlsanity.com/paper/2503.11290/) (2025)
- [Behind the Screens: Uncovering Bias in AI-Driven Video Interview Assessments Using Counterfactuals](https://mlsanity.com/paper/2505.12114/) (2025)
- [When LLMs Team Up: The Emergence of Collaborative Affective Computing](https://mlsanity.com/paper/2506.01698/) (2025)
- [Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model](https://mlsanity.com/paper/2404.09045/) (2024)
- [MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible Affective Computing](https://mlsanity.com/paper/2409.07256/) (2024)
- [MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge](https://mlsanity.com/paper/2505.24493/) (2025)
- [Do Vision Language Models Understand Human Engagement in Games?](https://mlsanity.com/paper/2603.18480/) (2026)
- [Recognizing Conditional Causal Relationships about Emotions and Their Corresponding Conditions](https://mlsanity.com/paper/2311.16579/) (2023)
- [Expansion Quantization Network: An Efficient Micro-emotion Annotation and Detection Framework](https://mlsanity.com/paper/2411.06160/) (2024)
- [Affect Models Have Weak Generalizability to Atypical Speech](https://mlsanity.com/paper/2504.16283/) (2025)
- [The Promise of Large Language Models in Digital Health: Evidence from Sentiment Analysis in Online Health Communities](https://mlsanity.com/paper/2508.14032/) (2025)
- [Beyond One-Size-Fits-All: Personalized Harmful Content Detection with In-Context Learning](https://mlsanity.com/paper/2511.05532/) (2025)
- [How Human is AI? Examining the Impact of Emotional Prompts on Artificial and Human and Responsiveness](https://mlsanity.com/paper/2601.05104/) (2026)
- [A Hormone-inspired Emotion Layer for Transformer language models (HELT)](https://mlsanity.com/paper/2605.13858/) (2026)
- [Steering Emotional Dynamics for Art Therapy: Controllable Narrative Script Generation through Hierarchically Guided LLM Agents](https://mlsanity.com/paper/2606.16481/) (2026)

## Abstract (arXiv)

With the rise of foundation models, a new artificial intelligence paradigm has emerged, by simply using general purpose foundation models with prompting to solve problems instead of training a separate machine learning model for each problem. Such models have been shown to have emergent properties of solving problems that they were not initially trained on. The studies for the effectiveness of such models are still quite limited. 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. We introduce a framework to evaluate the ChatGPT models on regression-based problems, such as intensity ranking problems, by modelling them as pairwise ranking classification. We compare ChatGPT against more traditional NLP methods, such as end-to-end recurrent neural networks and transformers. The results demonstrate the emergent abilities of the ChatGPT models on a wide range of affective computing problems, where GPT-3.5 and especially GPT-4 have shown strong performance on many problems, particularly the ones related to sentiment, emotions, or toxicity. The ChatGPT models fell short for problems with implicit signals, such as engagement measurement and subjectivity detection.