THE DEVELOPMENT OF A DEPRESSION RISK ANALYSIS MODEL USING ONLINE SOCIAL NETWORK DATA - Tipprasert, Suda; Angskun, Thara; Angskun, Jitimon (Srinakharinwirot University Journal of Sciences and Technology, 2024)
The number of people with depression is constantly increasing. Depressed people are not treated and express behavior via social network posts. Thus, a depression risk analysis model is proposed using online social network data. This research collects data from the patient depression questionnaire (PHQ-9) and Twitter comment data. The Twitter data is collected from 405 Twitter users, 178 depressed people and 114 regular people. A hybrid machine learning technique is applied as the model construction and compared with four machine learning techniques Support Vector Machine, Naïve Bayes, Decision Tree, Deep Learning, Random Forest. The experimental results revealed that Hybrid machine learning technique achieved higher F-measure than other machine learning techniques. Moreover, the results indicated that the appropriate attributes for modeling in this research were all features which consisted of Demographic Characteristics, Twitter User’s Information, Text, and Emoticons.