  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.
