Efficacy of ChatGPT in Cantonese Sentiment Analysis: Comparative Study

• Published in: Journal of medical Internet research Vol. 26; no. 3; p. e51069
• Main Authors: Fu, Ziru, Hsu, Yu Cheng, Chan, Christian S, Lau, Chaak Ming, Liu, Joyce, Yip, Paul Siu Fai
• Format: Journal Article
• Language: English
• Published: Canada Journal of Medical Internet Research 30.01.2024; Gunther Eysenbach MD MPH, Associate Professor; JMIR Publications
• Subjects: Accuracy; Analysis; Artificial intelligence; Asian People; Cantonese; Chatbots; Chinese languages; Comparative studies; Computational linguistics; Counseling services; Counselors; Dictionaries; Efficacy; Hong Kong; Humans; Language; Language processing; Linear Models; Linguistics; Machine learning; Mental health; Multilingualism; Natural language interfaces; Neural networks; Original Paper; Psychiatric services; Psychotherapy; Sentiment analysis; Short term memory; Social networks; Uniqueness; Hong Kong; United Kingdom; China; Hong Kong China; NLP; counseling; natural language processing; ChatGPT; sentiment analysis; Cantonese
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/51069

Sentiment analysis is a significant yet difficult task in natural language processing. The linguistic peculiarities of Cantonese, including its high similarity with Standard Chinese, its grammatical and lexical uniqueness, and its colloquialism and multilingualism, make it different from other languages and pose additional challenges to sentiment analysis. Recent advances in models such as ChatGPT offer potential viable solutions. This study investigated the efficacy of GPT-3.5 and GPT-4 in Cantonese sentiment analysis in the context of web-based counseling and compared their performance with other mainstream methods, including lexicon-based methods and machine learning approaches. We analyzed transcripts from a web-based, text-based counseling service in Hong Kong, including a total of 131 individual counseling sessions and 6169 messages between counselors and help-seekers. First, a codebook was developed for human annotation. A simple prompt ("Is the sentiment of this Cantonese text positive, neutral, or negative? Respond with the sentiment label only.") was then given to GPT-3.5 and GPT-4 to label each message's sentiment. GPT-3.5 and GPT-4's performance was compared with a lexicon-based method and 3 state-of-the-art models, including linear regression, support vector machines, and long short-term memory neural networks. Our findings revealed ChatGPT's remarkable accuracy in sentiment classification, with GPT-3.5 and GPT-4, respectively, achieving 92.1% (5682/6169) and 95.3% (5880/6169) accuracy in identifying positive, neutral, and negative sentiment, thereby outperforming the traditional lexicon-based method, which had an accuracy of 37.2% (2295/6169), and the 3 machine learning models, which had accuracies ranging from 66% (4072/6169) to 70.9% (4374/6169). Among many text analysis techniques, ChatGPT demonstrates superior accuracy and emerges as a promising tool for Cantonese sentiment analysis. This study also highlights ChatGPT's applicability in real-world scenarios, such as monitoring the quality of text-based counseling services and detecting message-level sentiments in vivo. The insights derived from this study pave the way for further exploration into the capabilities of ChatGPT in the context of underresourced languages and specialized domains like psychotherapy and natural language processing.