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BACKGROUND Sentiment analysis, in general, and in Cantonese, in particular, remain a significant yet challenging task in natural language processing. One major barrier is Cantonese’s lack of standardised corpus and its nature as a spoken language. OBJECTIVE Our study investigated using ChatGPT for sentiment analysis in online Cantonese counseling text and compared its performance with other mainstream methods. METHODS This study investigated the application of ChatGPT/GPT for Cantonese sentiment analysis using transcripts from an online text-based counseling service in Hong Kong. A total of 131 individual counseling sessions, with 6,169 messages between counselors and help-seekers were included in this study. First, a codebook was developed for human annotation. Then a simple prompt, “Is the sentiment of this Cantonese text positive, neutral, or negative? Respond with the sentiment label only.”, was given to ChatGPT 3.5 and GPT-4 to label each message’s sentiment. ChatGPT’s performance was compared with one lexicon-based method and three state-of-the-art models. RESULTS The accuracy in identifying positive, neutral, and negative feelings of ChatGPT 3.5 and GPT-4 was 92.1% and 95.3%, respectively, which outperformed the lexicon-based methods and machine learning models. CONCLUSIONS ChatGPT/GPT stands out among many existing text analysis techniques in terms of accuracy and could be considered a useful tool for analyzing Cantonese sentiments.