Dataset Information

Efficacy of ChatGPT in Cantonese Sentiment Analysis: Comparative Study.

ABSTRACT:

Background

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.

Objective

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.

Methods

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.

Results

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).

Conclusions

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.

SUBMITTER: Fu Z

PROVIDER: S-EPMC10865189 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Efficacy of ChatGPT in Cantonese Sentiment Analysis: Comparative Study.

Fu Ziru Z Hsu Yu Cheng YC Chan Christian S CS Lau Chaak Ming CM Liu Joyce J Yip Paul Siu Fai PSF

Journal of medical Internet research 20240130

<h4>Background</h4>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.<h4>Objective</h4>This study investigated the e ...[more]

PMID: 38289662

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Dataset Information

Efficacy of ChatGPT in Cantonese Sentiment Analysis: Comparative Study.

Background

Objective

Methods

Results

Conclusions

Publications

Efficacy of ChatGPT in Cantonese Sentiment Analysis: Comparative Study.

Similar Datasets

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