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.

本研究探讨了 ChatGPT 作为数据标记工具用于不同情感分析任务中的应用，结果表明相较于基于词汇表的算法，ChatGPT 在准确性方面有了显著提高，可用于不同的事件和任务情感分析的标注工作。

利用 ChatGPT 作为文本标注工具进行情感分析

Leveraging ChatGPT As Text Annotation Tool For Sentiment Analysis

Discovering what other people think has always been a key aspect of our
information-gathering strategy. People can now actively utilize information
technology to seek out and comprehend the ideas of others, thanks to the
increased availability and popularity of opinion-rich resources such as online
review sites and personal blogs. Because of its crucial function in
understanding people's opinions, sentiment analysis (SA) is a crucial task.
Existing research, on the other hand, is primarily focused on the English
language, with just a small amount of study devoted to low-resource languages.
For sentiment analysis, this work presented a new multi-class Urdu dataset
based on user evaluations. The tweeter website was used to get Urdu dataset.
Our proposed dataset includes 10,000 reviews that have been carefully
classified into two categories by human experts: positive, negative. The
primary purpose of this research is to construct a manually annotated dataset
for Urdu sentiment analysis and to establish the baseline result. Five
different lexicon- and rule-based algorithms including Naivebayes, Stanza,
Textblob, Vader, and Flair are employed and the experimental results show that
Flair with an accuracy of 70% outperforms other tested algorithms.

本研究提出了一个基于用户评价的新型多类乌尔都数据集，旨在为乌尔都情感分析构建手动注释的数据集，并建立基准结果。使用五种不同的词库和基于规则的算法，最终实验结果表明，Flair 的准确率为 70%，优于其他测试的算法。