@article{ref1,
title="Applying text mining methods to suicide research",
journal="Suicide and life-threatening behavior",
year="2021",
author="Cheng, Qijin and Lui, Carrie S. M.",
volume="51",
number="1",
pages="137-147",
abstract="OBJECTIVE: 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. <br><br>METHOD: 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. <br><br>RESULTS: 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. <br><br>CONCLUSIONS: Computerized text mining methods can help to scale up content analysis capacity and efficiency and uncover new insights and perspectives for suicide research.<p /> <p>Language: en</p>",
language="en",
issn="0363-0234",
doi="10.1111/sltb.12680",
url="http://dx.doi.org/10.1111/sltb.12680"
}