Department of Social Work, The Chinese University of Hong Kong, Hong Kong SAR, China.
Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China.
Suicide Life Threat Behav. 2021 Feb;51(1):137-147. doi: 10.1111/sltb.12680.
To introduce the research methods of computerized text mining and its possible applications in suicide research and to demonstrate the procedures of applying a specific text mining area, document classification, to a suicide-related study.
A systematic search of academic papers that applied text mining methods to suicide research was conducted. Relevant papers were reviewed focusing on their research objectives and sources of data. Furthermore, a case of using natural language processing and document classification methods to analyze a large amount of suicide news was elaborated to showcase the methods.
Eighty-six papers using text mining methods for suicide research have been published since 2001. The most common research objective (72.1%) was to classify which documents exhibit suicide risk or were written by suicidal people. The most frequently used data source was online social media posts (45.3%), followed by e-healthcare records (25.6%). For the news classification case, the top three classifiers trained for classification tasks achieved 84% or higher accuracy.
Computerized text mining methods can help to scale up content analysis capacity and efficiency and uncover new insights and perspectives for suicide research.
介绍计算机文本挖掘的研究方法及其在自杀研究中的可能应用，并展示应用特定文本挖掘领域（文档分类）进行自杀相关研究的步骤。
系统检索了应用文本挖掘方法进行自杀研究的学术论文，重点关注其研究目标和数据来源。此外，还详细阐述了使用自然语言处理和文档分类方法分析大量自杀新闻的案例，以展示这些方法。
自 2001 年以来，已有 86 篇使用文本挖掘方法进行自杀研究的论文发表。最常见的研究目标（72.1%）是对哪些文档表现出自杀风险或由自杀者撰写进行分类。最常使用的数据来源是在线社交媒体帖子（45.3%），其次是电子医疗记录（25.6%）。对于新闻分类案例，针对分类任务训练的前三个分类器的准确率达到 84%或更高。
计算机化文本挖掘方法可以帮助扩大内容分析的规模和效率，并为自杀研究揭示新的见解和视角。
