华东师范大学学报（自然科学版） >

2025 , Vol. 2025 >Issue 1: 124 - 137

DOI: https://doi.org/10.3969/j.issn.1000-5641.2025.01.010

计算机科学

基于标签感知增强的社交媒体心理亚健康归因方法

  • 梁怡萍 ,
  • 肖路巍 ,
  • 王琳琳
展开
  • 华东师范大学 计算机科学与技术学院, 上海　200062
王琳琳, 女, 研究员, 博士生导师, 研究方向为自然语言处理. E-mail: llwang@cs.ecnu.edu.cn

收稿日期: 2024-01-09

  网络出版日期: 2025-01-20

版权

华东师范大学学报期刊社, 2025, 版权所有，未经授权，不得转载、摘编本刊文章，不得使用本刊的版式设计。
收起

Label-perception augmented causal analysis of mental health over social media

  • Yiping LIANG ,
  • Luwei XIAO ,
  • Linlin WANG
Expand
  • School of Computer Science and Technology, East China Normal University, Shanghai　200062, China

Received date: 2024-01-09

  Online published: 2025-01-20

Copyright

, 2025, Copyright reserved © 2025.
Fold

摘要

在大量的网络社交媒体中, 存在一些表达了潜在的心理健康障碍和精神疾病的帖子, 根据帖子文本识别用户产生心理健康障碍的原因是一项重要任务. 观察这些帖子发现, 其上下文中存在标签共现现象, 即上下文中同时出现了多个候选标签的语义, 干扰了标签表征的建模与预测. 为缓解该现象带来的影响, 提出了一种标签感知增强分类的方法, 该方法利用大规模预训练语言模型识别潜在的候选标签, 并通过估计样本独立的标签语义强度作为增强数据以消减共现标签带来的噪声, 基于增强数据构建了性能良好的预训练语言模型分类器. 在数据集Intent_SDCNL和SAD上进行的实验验证了该方法的有效性.

关键词： 心理亚健康归因; 自然语言处理; 提示学习

本文引用格式

梁怡萍 , 肖路巍 , 王琳琳 . 基于标签感知增强的社交媒体心理亚健康归因方法[J]. 华东师范大学学报（自然科学版）, 2025 , 2025(1) : 124 -137 . DOI: 10.3969/j.issn.1000-5641.2025.01.010

Abstract

Online social media are frequently used by people as a way of expressing their thoughts and feelings. Among the vast amounts of online posts, there may be more concerning ones expressing potential grievances and mental illnesses. Identifying these along with potential causes of mental health problems is an important task. Observing these posts, it is found that there is a label co-occurrence phenomenon in contexts, i.e., the semantics of multiple candidate labels appear in the context of one sample, which interferes with the modeling and prediction of label patterns. To mitigate the impact of this phenomenon, we propose a label-aware data augmentation method, which leverages large-scale pre-trained language models with excellent text comprehension capability to identify potential candidate labels, abates the noise from irrelevant co-occurring labels by estimating sample-independent label semantic strengths, and constructs well-performing classifiers with pre-trained language models. Extensive experiments validate the effectiveness of our model on the recent datasets Intent_SDCNL and SAD.

Key words： causal analysis of mental health; natural language processing; prompt learning

参考文献

1 MAURIELLO M L, LINCOLN T, HON G, et al. SAD: A stress annotated dataset for recognizing everyday stressors in SMS-like conversational systems [C]// Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. 2021: 399.
2 GARG M, SAXENA C, SAHA S, et al. CAMS: An annotated corpus for causal analysis of mental health issues in social media posts [C]// Proceedings of the Thirteenth Language Resources and Evaluation Conference. 2022: 6387-6396.
3 SAXENA C, GARG M, ANSARI G. Explainable causal analysis of mental health on social media data [C]// International Conference on Neural Information Processing. 2022: 172-183.
4 YANG K, JI S, ZHANG T, et al. Towards interpretable mental health analysis with large language models [C]// Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023: 6056-6077.
5 YANG K, ZHANG T, KUANG Z, et al. MentaLLaMA: Interpretable mental health analysis on social media with large language models [EB/OL]. (2023-09-24)[2024-01-06]. https://arxiv.org/abs/2309.13567.
6 ZHOU Y, MURESANU A I, HAN Z, et al. Large language models are human-level prompt engineers [EB/OL]. (2023-03-10)[2024-01-06]. https://arxiv.org/abs/2211.01910.
7 XU N, QIAO C, GENG X, et al.. Instance-dependent partial label learning. Advances in Neural Information Processing Systems, 2021, 34, 27119- 27130.
8 RIBEIRO M T, SINGH S, GUESTRIN C. “Why should I trust you?”: Explaining the predictions of any classifier [C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016: 1135-1144.
9 ZHAO W X, ZHOU K, LI J, et al. A survey of large language models [EB/OL]. (2023-11-24)[2024-01-06]. https://arxiv.org/abs/2303.18223.
10 TOUVRON H, LAVRIL T, IZACARD G, et al. LLaMA: Open and efficient foundation language models [EB/OL]. (2023-02-27)[2024-01-06]. https://arxiv.org/abs/2302.13971.
11 VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017: 6000-6010.
12 DEVLIN J, CHANG M W, LEE K, et al. BERt: Pre-training of deep bidirectional transformers for language understanding [EB/OL]. (2019-05-24)[2024-01-06]. https://arxiv.org/abs/1810.04805.
13 LIU P, YUAN W, FU J, et al.. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 2023, 55 (9): 195- 35.
14 LU Y, BARTOLO M, MOORE A, et al. Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity [C]// Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. 2022: 8086-8098.
15 HOFFMANN J, BORGEAUD S, MENSCH A, et al.. An empirical analysis of compute-optimal large language model training. Advances in Neural Information Processing Systems, 2022, 35, 30016- 30030.
16 BROWN T, MANN B, RYDER N, et al.. Language models are few-shot learners. Advances in Neural Information Processing Systems, 2020, 33, 1877- 1901.
17 CHUNG H W, HOU L, LONGPRE S, et al. Scaling instruction-finetuned language models [EB/OL]. (2022-12-06)[2024-01-06]. https://arxiv.org/abs/2210.11416.
18 WEI J, WANG X, SCHUURMANS D, et al.. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 2022, 35, 24824- 24837.
19 BLEI D M, KUCUKELBIR A, MCAULIFFE J D.. Variational inference: A review for statisticians. Journal of the American Statistical Association, 2017, 112 (518): 859- 877.
20 HOFFMAN M, BLEI D M, WANG C, et al.. Stochastic variational inference. Journal of Machine Learning Research, 2013, 14, 1303- 1347.
21 RANGANATH R, GERRISH S, BLEI D. Black box variational inference [C]// Proceedings of the 17th International Conference on Articial Intelligence and Statistics. 2014: 814-822.
22 OPPER M, SAAD D. Advanced Mean Field Methods: Theory and Practice [M]. Cambridge: Massachusetts Institute of Technology Press, 2001.
23 PRYZANT R, ITER D, LI J, et al. Automatic prompt optimization with “gradient descent” and beam search [C]// Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023: 7957-7968.
24 ZHU X. Semi-supervised Learning with Graphs [M]. Pennsylvania, USA: Carnegie Mellon University Press, 2005.
25 KIPF T N, WELLING M. Variational graph auto-encoders [EB/OL]. (2016-11-21)[2024-01-06]. https://arxiv.org/abs/1611.07308.
26 KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks [EB/OL]. (2017-02-22)[2024-01-06]. https://arxiv.org/abs/1609.02907.
27 FIGURNOV M, MOHAMED S, MNIH A. Implicit reparameterization gradients [C]// Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018: 439-450.
28 POPESCU M C, BALAS V E, PERESCU-POPESCU L, et al.. Multilayer perceptron and neural networks. WSEAS Transactions on Circuits and Systems, 2009, 8 (7): 579- 588.
Options
文章导航

/

〈 〉