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返回卷期清單 (Vol.61, No.1 / 2024 / 3)
Vol.61, No.1
/
2024 / 3
/
pp. 5 - 27
DOI:
心理健康素養推文自動分類之研究
A Study on the Automatic Classification of Tweets Related to Mental Health Literacy
133
35
作者
曾偉紘 Wei-Hung Tseng
(國立臺灣師範大學圖書資訊學研究所 Graduate Institute of Library & Information Studies, National Taiwan Normal University, Taipei, Taiwan)
連盈如 Yin-Ju Lien
(國立臺灣師範大學健康促進與衛生教育學系 Department of Health Promotion and Health Education, National Taiwan Normal University, Taipei, Taiwan)
陳昭慧 Chao-Hui Chen
(國立臺灣師範大學健康促進與衛生教育學系 Department of Health Promotion and Health Education, National Taiwan Normal University, Taipei, Taiwan)
謝建成 Jiann-Cherng Shieh
(國立臺灣師範大學圖書資訊學研究所 Graduate Institute of Library & Information Studies, National Taiwan Normal University, Taipei, Taiwan)
曾元顯 Yuen-Hsien Tseng *
(國立臺灣師範大學圖書資訊學研究所 Graduate Institute of Library & Information Studies, National Taiwan Normal University, Taipei, Taiwan)
曾偉紘 Wei-Hung Tseng
國立臺灣師範大學圖書資訊學研究所 Graduate Institute of Library & Information Studies, National Taiwan Normal University, Taipei, Taiwan
連盈如 Yin-Ju Lien
國立臺灣師範大學健康促進與衛生教育學系 Department of Health Promotion and Health Education, National Taiwan Normal University, Taipei, Taiwan
陳昭慧 Chao-Hui Chen
國立臺灣師範大學健康促進與衛生教育學系 Department of Health Promotion and Health Education, National Taiwan Normal University, Taipei, Taiwan
謝建成 Jiann-Cherng Shieh
國立臺灣師範大學圖書資訊學研究所 Graduate Institute of Library & Information Studies, National Taiwan Normal University, Taipei, Taiwan
曾元顯 Yuen-Hsien Tseng *
國立臺灣師範大學圖書資訊學研究所 Graduate Institute of Library & Information Studies, National Taiwan Normal University, Taipei, Taiwan
中文摘要

【此篇文章之同儕評閱意見報告（Open Point）及導讀簡報（InSight Point）請至本刊網站查閱。https://doi.org/10.6120/JoEMLS.202403_61(1).0004.RS.CM】

推特上不乏使用者貼出描述心情的各式推文，分析這些推文，可協助瞭解個體的心理狀態，對促進大眾心理健康的研究將有所助益。本研究擬對推文中關於心理健康素養方面的簡短文本，進行自動分類。使用包括傳統機器學習以及BERT、SetFit、GPT-3、 GPT-4 等人工智慧的技術，將其自動分類到五個面向中的11個題項，每個題項都有五個相關強度分數。期望在有限的人工標記的訓練資料下，機器預測的成效要到0.8以上，達到機器有效協助心理健康研究的目的。研究結果顯示使用SetFit 進行自動分類，多數題項都能達到MacroF1約0.8的標準，只有兩個題項成效在0.65左 右。本研究的貢獻之一，在呈現並比較多種自然語言處理派典，在這些困難任務中，其文字理解與分析上的成效。

英文摘要

【Please visit JoEMLS website to read the Peer Review Report (Open Point) and Article Summary (InSight Point) of the article. https://doi.org/10.6120/JoEMLS.202403_61(1).0004.RS.CM】

Users on Twitter often post various tweets describing their moods. Analyzing these tweets can aid in understanding an individual’s psychological state, which will be beneficial to research aimed at promoting public mental health. This study intends to perform automatic classification on tweets related to mental health literacy. Techniques including traditional machine learning as well as AI technologies like BERT, SetFit, GPT-3, and GPT-4 are used to automatically classify them into 11 items across five dimensions, with each item having five related intensity scores. The goal is to achieve a machine prediction effectiveness of over 0.8 with limited human-annotated training data, to ensure the machine can effectively assist in mental health research. The results show that using SetFit, most items can achieve a Macro F1 score of about 0.8, with only two items scoring around 0.65. T he contribution of this study lies in presenting and comparing the effectiveness of various natural language processing paradigms in text comprehension and analysis on these difficult tasks.

中文關鍵字

機器學習; 深度學習; 少樣本微調; 自動分類

英文關鍵字

Machine learning; Deep learning; Few-shot fine - tuning; Automatic classification