Abstract:
The mental health implications of Diabetes can be mitigated through the presence
of a social support system, and social media platforms offer a convenient avenue
for fostering such support, even among healthcare professionals and patients. This
study introduces novel machine learning models tailored for multilabel social support
classification of Filipino-English Endocrinology Facebook comments. The objective
is to effectively categorize comments into distinct types of support, including informational,
emotional, appraisal, instrumental, and spam, thereby enabling healthcare
professionals to efficiently manage their social media groups. The dataset underwent
manual data cleaning and was subsequently divided into training and testing
sets. Preprocessing techniques encompassing lowercasing, tokenization, and TF-IDF
vectorization were employed on both sets. To address dataset imbalances, data augmentation
techniques were implemented. Notably, the LP-SVM model emerged as
the top performer and was seamlessly integrated into the CommentSense application.
These findings enhance our comprehension of social support dynamics and furnish
practitioners with a user-friendly tool for social support text classification.
