# Leveraging ChatGPT As Text Annotation Tool For Sentiment Analysis

arXiv: https://arxiv.org/abs/2306.17177 · HTML: https://mlsanity.com/paper/2306.17177/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 18 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023)

## Built on by (most cited first, 14 of 14)

- [Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era](https://mlsanity.com/paper/2403.08946/) (2024)
- [EmoBench-M: Benchmarking Emotional Intelligence for Multimodal Large Language Models](https://mlsanity.com/paper/2502.04424/) (2025)
- [Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges](https://mlsanity.com/paper/2403.02990/) (2024)
- [Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective](https://mlsanity.com/paper/2408.04638/) (2024)
- [DepressionEmo: A novel dataset for multilabel classification of depression emotions](https://mlsanity.com/paper/2401.04655/) (2024)
- [From Voices to Validity: Leveraging Large Language Models (LLMs) for Textual Analysis of Policy Stakeholder Interviews](https://mlsanity.com/paper/2312.01202/) (2023)
- [Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications](https://mlsanity.com/paper/2402.01681/) (2024)
- [Large corpora and large language models: a replicable method for automating grammatical annotation](https://mlsanity.com/paper/2411.11260/) (2024)
- [Irony Detection, Reasoning and Understanding in Zero-shot Learning](https://mlsanity.com/paper/2501.16884/) (2025)
- [Augmenting Image Annotation: A Human-LMM Collaborative Framework for Efficient Object Selection and Label Generation](https://mlsanity.com/paper/2503.11096/) (2025)
- [Human-LLM Collaborative Construction of a Cantonese Emotion Lexicon](https://mlsanity.com/paper/2410.11526/) (2024)
- [Unlocking Cross-Lingual Sentiment Analysis through Emoji Interpretation: A Multimodal Generative AI Approach](https://mlsanity.com/paper/2412.17255/) (2024)
- [The Promise of Large Language Models in Digital Health: Evidence from Sentiment Analysis in Online Health Communities](https://mlsanity.com/paper/2508.14032/) (2025)
- [A Retail-Corpus for Aspect-Based Sentiment Analysis with Large Language Models](https://mlsanity.com/paper/2508.17994/) (2025)

## Abstract (arXiv)

Sentiment analysis is a well-known natural language processing task that involves identifying the emotional tone or polarity of a given piece of text. With the growth of social media and other online platforms, sentiment analysis has become increasingly crucial for businesses and organizations seeking to monitor and comprehend customer feedback as well as opinions. Supervised learning algorithms have been popularly employed for this task, but they require human-annotated text to create the classifier. To overcome this challenge, lexicon-based tools have been used. A drawback of lexicon-based algorithms is their reliance on pre-defined sentiment lexicons, which may not capture the full range of sentiments in natural language. ChatGPT is a new product of OpenAI and has emerged as the most popular AI product. It can answer questions on various topics and tasks. This study explores the use of ChatGPT as a tool for data labeling for different sentiment analysis tasks. It is evaluated on two distinct sentiment analysis datasets with varying purposes. The results demonstrate that ChatGPT outperforms other lexicon-based unsupervised methods with significant improvements in overall accuracy. Specifically, compared to the best-performing lexical-based algorithms, ChatGPT achieves a remarkable increase in accuracy of 20% for the tweets dataset and approximately 25% for the Amazon reviews dataset. These findings highlight the exceptional performance of ChatGPT in sentiment analysis tasks, surpassing existing lexicon-based approaches by a significant margin. The evidence suggests it can be used for annotation on different sentiment analysis events and taskss.