Leveraging ChatGPT As Text Annotation Tool For Sentiment Analysis
Mohammad Belal, James She, Simon Wong

TL;DR
This paper investigates using ChatGPT as an automatic data annotation tool for sentiment analysis, demonstrating its superior accuracy over traditional lexicon-based methods across multiple datasets.
Contribution
It introduces ChatGPT as a novel, effective tool for sentiment data labeling, significantly improving annotation accuracy compared to existing lexicon-based approaches.
Findings
ChatGPT outperforms lexicon-based methods with 20-25% accuracy improvements.
It is effective across different sentiment analysis datasets.
ChatGPT can be used for diverse sentiment annotation tasks.
Abstract
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…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Topic Modeling · Stock Market Forecasting Methods
