Enhanced Labeling Technique for Reddit Text and Fine-Tuned Longformer Models for Classifying Depression Severity in English and Luganda
Abstract
Depression is a global burden and one of the most challenging mental health conditions to control. Experts can detect its severity early using the Beck Depression Inventory (BDI) questionnaire, administer appropriate medication to patients, and impede its progression. Due to the fear of potential stigmatization, many patients turn to social media platforms like Reddit for advice and assistance at various stages of their journey. This research extracts text from Reddit to facilitate the diagnostic process. It employs a proposed labeling approach to categorize the text and subsequently fine-tunes the Longformer model. The model's performance is compared against baseline models, including Naive Bayes, Random Forest, Support Vector Machines, and Gradient Boosting. Our findings reveal that the Longformer model outperforms the baseline models in both English (48%) and Luganda (45%) languages on a custom-made dataset.
Keywords
Cite
@article{arxiv.2401.14240,
title = {Enhanced Labeling Technique for Reddit Text and Fine-Tuned Longformer Models for Classifying Depression Severity in English and Luganda},
author = {Richard Kimera and Daniela N. Rim and Joseph Kirabira and Ubong Godwin Udomah and Heeyoul Choi},
journal= {arXiv preprint arXiv:2401.14240},
year = {2024}
}
Comments
In IEEE Proceedings of the 14th International Conference on ICT Convergence (ICTC), Jeju, Korea, October 2023