A two-stage text-to-emotion depressive disorder screening assistance from online community text
3
Issued Date
2024
Copyright Date
2020
Resource Type
Language
eng
File Type
application/pdf
No. of Pages/File Size
x, 90 leaves : ill.
Access Rights
open access
Rights
ผลงานนี้เป็นลิขสิทธิ์ของมหาวิทยาลัยมหิดล ขอสงวนไว้สำหรับเพื่อการศึกษาเท่านั้น ต้องอ้างอิงแหล่งที่มา ห้ามดัดแปลงเนื้อหา และห้ามนำไปใช้เพื่อการค้า
Rights Holder(s)
Mahidol University
Bibliographic Citation
Thesis (M.Sc. (Information Technology Management))--Mahidol University, 2020
Suggested Citation
Supawit Marerngsit A two-stage text-to-emotion depressive disorder screening assistance from online community text. Thesis (M.Sc. (Information Technology Management))--Mahidol University, 2020. Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/92083
Title
A two-stage text-to-emotion depressive disorder screening assistance from online community text
Alternative Title(s)
การช่วยคัดกรองโรคซึมเศร้าจากข้อความไปหาอารมณ์สองขั้วในข้อความในสังคมออนไลน์
Author(s)
Abstract
Major Depressive Disorder (MDD), is one of the most serious mental diseases that can occur in anyone regardless of age or gender. Even though the symptoms of MDD are similar to regular emotional sadness, its impact is much worse and cloud lead to committing suicide. Nowadays, almost all people regularly use social media to communicate and express their opinions where the emotion could be impliedly illustrated through the textual conversation which leads to the implication to the possible sign of depression. This research presents a two-stage predictive model for MDD's risk screening assistance by using emotion weight based on textual data from social community. The messages are identified in six emotional dimensions angry, bored, excited, scared, happy, and sad. These emotions are then categorized into 6 emotion-based clusters using DBSCAN algorithm and examined by medical and psychological experts with Patient Health Questionnaire-2. The labeled clusters are used to train and optimize classification models for each screening question with several algorithms Decision Tree, Random Forest, and Gradient Boosted Tree. The experimental results show that the Gradient Boosted Tree is the best model for Q1 and the Random Forest is the best model for Q2 with 98.32% and 99.98%, accuracy respectively. This research will be beneficial in the further study in identifying depression from text-based emotion.
Description
Information Technology Management (Mahidol University 2020)
Degree Name
Master of Science
Degree Level
Master's degree
Degree Department
Faculty of Engineering
Degree Discipline
Information Technology Management
Degree Grantor(s)
Mahidol University