Qijin Cheng1, Carrie S M Lui2. 1. Department of Social Work, The Chinese University of Hong Kong, Hong Kong SAR, China. 2. Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China.
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. 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. 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. CONCLUSIONS: Computerized text mining methods can help to scale up content analysis capacity and efficiency and uncover new insights and perspectives for suicide research.
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. 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. 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. CONCLUSIONS: Computerized text mining methods can help to scale up content analysis capacity and efficiency and uncover new insights and perspectives for suicide research.