JSAI2022

Presentation information

Organized Session

Organized Session » OS-13

[1J4-OS-13a] 医療言語処理の拡張と連携(1/2)

Tue. Jun 14, 2022 2:20 PM - 4:00 PM Room J (Room J)

オーガナイザ:矢田 竣太郎(奈良先端科学技術大学院大学)[現地]、荒牧 英治(奈良先端科学技術大学院大学)、河添 悦昌(東京大学)

3:20 PM - 3:40 PM

[1J4-OS-13a-04] Mental Health Classification using Large Scale Tweet Dataset

〇Ryo Takasu1, Hironobu Nakamura2, Taishiro Kishimoto3, Yoshinobu Kano1 (1. Shizuoka University, 2. Tokyo Medical and Dental University, 3. Keio University)

Keywords:medical care, social media

Mental health is socially an important issue in society. In recent years, the mental health issue has been closely related to activities on the Internet. We have developed a system for classifying Twitter users whether they have mental health issues. We assumed that accounts that match specific patterns are likely to have mental health issues, and collected positive and negative examples based on this assumption. In order to investigate the possibility to classify user utterances without mental health specific keywords, we discarded tweets which include such keywords, in contrast to keyword dependent previous studies. We trained and compared classification performances of BERT, LSTM, and SVM. Regarding BERT, we originally pre-trained the model using the large-scale tweet data. BERT achieved high classification performance with Accuracy 0.83, Recall 0.8, Precision 0.88, and F1-score 0.84 in the best performance case.

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