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Sentiment analysis is a well-known natural language processing task thatinvolves identifying the emotional tone or polarity of a given piece of text.With the growth of social media and other online platforms, sentiment analysishas become increasingly crucial for businesses and organizations seeking tomonitor and comprehend customer feedback as well as opinions. Supervisedlearning algorithms have been popularly employed for this task, but theyrequire human-annotated text to create the classifier. To overcome thischallenge, lexicon-based tools have been used. A drawback of lexicon-basedalgorithms is their reliance on pre-defined sentiment lexicons, which may notcapture the full range of sentiments in natural language. ChatGPT is a newproduct of OpenAI and has emerged as the most popular AI product. It can answerquestions on various topics and tasks. This study explores the use of ChatGPTas a tool for data labeling for different sentiment analysis tasks. It isevaluated on two distinct sentiment analysis datasets with varying purposes.The results demonstrate that ChatGPT outperforms other lexicon-basedunsupervised methods with significant improvements in overall accuracy.Specifically, compared to the best-performing lexical-based algorithms, ChatGPTachieves a remarkable increase in accuracy of 20% for the tweets dataset andapproximately 25% for the Amazon reviews dataset. These findings highlight theexceptional performance of ChatGPT in sentiment analysis tasks, surpassingexisting lexicon-based approaches by a significant margin. The evidencesuggests it can be used for annotation on different sentiment analysis eventsand taskss.

Leveraging ChatGPT As Text Annotation Tool For Sentiment Analysis