ChatGPT as a Text Annotation Tool to Evaluate Sentiment Analysis on South African Financial Institutions

Social media platforms play a significant role in analyzing customer perceptions of financial products and services in today’s culture. These platforms facilitate the immediate and in-depth sharing of thoughts and experiences, offering valuable insights into consumer behaviour. Any customer looking for such a service would surf the internet for reviews and ratings before making a decision, which usually influences their ultimate pick. Feedback and suggestions from friends, family, and coworkers improve customer experiences. Customer reviews play a crucial role in shaping the reputation and profitability of businesses and products offered by financial institutions, often serving as the final assessment of quality and satisfaction during decision-making. Therefore, it is paramount for decision-makers to carefully evaluate customer feedback and understand the sentiment expressed in a given piece of text, which could lead to equity trading, and credit market assessment, and offer invaluable insights that boost the financial performance of the institution. Previous research has used human-annotated text, such as lexicon-based methods, to train machine learning models for sentiment analysis, but the approach did not capture the full range of structure and semantic relationships in natural language. Therefore, our research aims to develop a more comprehensive and accurate sentiment analysis model using advanced natural language processing techniques that could answer questions on various subjects and tasks. To do this, we first crawled customer reviews on Hellopeter, a popular review site, and financial data on the top five financial institutions listed on the Johannesburg Stock Exchange (JSE) in South Africa. After that, we used OpenAI’s ChatGPT as a zero-short learning model to generate human-like annotation tools for different sentiment tasks. The OpenAI ChatGPT feature vector was subsequently fed into BERT, BiLSTM, and a SoftMax function to detect and identify the sentiment of a given sentence. Lastly, we use feature vectors with oversampling methods to address the imbalanced data dilemma and visualise the contribution features of the given piece of text for the customer reviewers. The experiments demonstrated that the method performed as well as or better than the latest and most effective methods on the tested datasets, yielding comparable results. When OpenAI’s ChatGPT was combined with pre-trained BERT and BiLSTM models, it did better overall, with an average score of 98.9%, an F1-measure of 97.7%, and an AUC of 91.90% when oversampling was used. The traditional lexicon-based model got an 86.68% score using SVM and logistic regression and an AUC of 91.90%. The study shows the exceptional performance of OpenAI ChatGPT in detecting the emotional tone or polarity of a given sentence in a customer review, which helps with annotation and understanding the sentiment analysis of an event and how it influences decisions and outcomes. In conclusion, these results underscore the significant advantages of incorporating customer sentiment analysis into financial analysis and decision-making processes as a valuable tool for understanding and prioritizing customer needs and preferences.