计算机技术与发展2024，Vol.34Issue(4)：153-161,9.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0023
基于不平衡社交媒体文本的抑郁症检测方法
A Detection Method for Depression Based on Imbalanced Social Media Text
摘要
Abstract
To address the challenges faced by the current depression detection model based on social media data,such as difficulties in handling imbalanced data and incomplete evaluation indicators,we propose a new approach called Document Adaptive Enhanced Bagging-τSS3(DAEB-τSS3).This method utilizes social media text data for depression detection and introduces a novel machine learning evaluation metric called GF(α,β)-Score.Building upon theτ-SS3 model,we incorporate confidence weighting to amplify the influence of certain data types.Additionally,we employ the Bagging method to enhance integrated learning,improving the sampling process from random sampling to layered sampling.This adaptive enhancement focuses on a select number of data documents,thereby improving the model's ability to handle imbalanced data.In the model evaluation stage,-we utilize GF-Score for automatic parameter selection and discard underperforming base learners,thereby enhancing the model's reliability and stability.Experimental results on the E-Risk2017 depression detection dataset demonstrate that DAEB-τSS3 exhibits superior adaptability to imbalanced datasets and outperforms τSS3,bi-directional long-term memory networks,and ERNIE 3.0 models.The average improvements in GF-Score,Fl-Score,and G-Mean Score are 13％,0.7％,and 26.9％,respectively,enabling more effective depression detection based on imbalanced social media texts.关键词
不平衡数据集/抑郁检测/集成学习/文本分类/社交媒体文本数据Key words
imbalanced dataset/depression detection/ensemble learning/text classification/social media text data分类
信息技术与安全科学引用本文复制引用
郭耀木,刘鹏,孙源乐,白其炜,张少华,刘建..基于不平衡社交媒体文本的抑郁症检测方法[J].计算机技术与发展,2024,34(4):153-161,9.基金项目
国家自然科学基金青年基金(JZ2019GJQN0385) （JZ2019GJQN0385）
安徽省大学生创新训练项目(S202210359346) （S202210359346）
合肥工业大学大学生创新训练项目(X202310359868) （X202310359868）
