Drug Abuse Research Trend Investigation with Text Mining

• Main Authors: Li-Wei Chou, .-, Kang-Ming Chang, .-, Ira Puspitasari, .-
• Format: Article PeerReviewed
• Language: English; English; English
• Published: Hindawi Publishing Corporation 2020
• Subjects: Q Science; Q Science (General); Q1-295 General; QA76.9.L63 Logic, Symbolic, mathematical and Computer logic
• Online Access: https://repository.unair.ac.id/113163/1/C04.%20Bukti%20Karya%20Ilmiah%20-%20Drug%20Abuse%20Research%20Trend.pdf; https://repository.unair.ac.id/113163/3/C04.%20Review%20dan%20Validasi.pdf; https://repository.unair.ac.id/113163/2/C04.%20Similarity_Drug%20Abuse%20Research%20Trend.pdf; https://repository.unair.ac.id/113163/; https://www.hindawi.com/journals/cmmm/2020/1030815/
• Institution: Universitas Airlangga

Drug abuse poses great physical and psychological harm to humans, thereby attracting scholarly attention. It often requires experience and time for a researcher, just entering this field, to find an appropriate method to study drug abuse issue. It is crucial for researchers to rapidly understand the existing research on a particular topic and be able to propose an effective new research method. Text mining analysis has been widely applied in recent years, and this study integrated the text mining method into a review of drug abuse research. Through searches for keywords related to the drug abuse, all related publications were identified and downloaded from PubMed. After removing the duplicate and incomplete literature, the retained data were imported for analysis through text mining. A total of 19,843 papers were analyzed, and the text mining technique was used to search for keyword and questionnaire types. The results showed the associations between these questionnaires, with the top five being the Addiction Severity Index (16.44%), the Quality of Life survey (5.01%), the Beck Depression Inventory (3.24%), the Addiction Research Center Inventory (2.81%), and the Profile of Mood States (1.10%). Specifically, the Addiction Severity Index was most commonly used in combination with Quality of Life scales. In conclusion, association analysis is useful to extract core knowledge. Researchers can learn and visualize the latest research trend.