Text-Mining Analyses of News Articles on Schizophrenia / 대한정신분열병학회지

ABSTRACT

Objectives@#In this study, we conducted an exploratory analysis of the current media trends on schizophrenia using text-mining methods. @*Methods@#First, web-crawling techniques extracted text data from 575 news articles in10 major newspapers between 2018 and 2019, which were selected by searching “schizophrenia” in the Naver News. We had developed document-term matrix (DTM) and/or term-document matrix (TDM) through pre-processing techniques. Through the use of DTM and TDM, frequency analysis, cooccurrence network analysis, and topic model analysis were conducted. @*Results@#Frequency analysis showed that keywords such as “police,” “mental illness,” “admission,” “patient,” “crime,” “apartment,” “lethal weapon,” “treatment,” “Jinju,” and “residents” were frequently mentioned in news articles on schizophrenia. Within the article text, many of these keywords were highly correlated with the term “schizophrenia” and were also interconnected with each other in the co-occurrence network. The latent Dirichlet allocation model presented 10 topics comprising a combination of keywords “police-Jinju,” “hospital-admission,” “research-finding,” “care-center,” “schizophrenia-symptom,” “society-issue,” “family-mind,” “woman-school,” and “disabled-facilities.” @*Conclusion@#The results of the present study highlight that in recent years, the media has been reporting violence in patients with schizophrenia, thereby raising an important issue of hospitalization and community management of patients with schizophrenia.