Early Detection for Depression in Indonesian Social Media Text with IndoBERT
DOI:
https://doi.org/10.32493/jiup.v11i2.58892Keywords:
Depression, Fine-Tuning, IndoBERT, Text-Classification, Twitter MiningAbstract
Depression has led to many cases of suicide across the world. Hence, early screenings are needed to provide the right medical treatments. Recent studies have attempted to use social media text which have become an integral part of people's daily lives to early detect depression, since users primarily post and express their thoughts in the form of text or media that reflect their current mental state. This study aims to fine-tune the pre-trained IndoBERT model on labeled Indonesian social media texts from X (previously Twitter) for binary depression-related text classification. The dataset used in this study, "Depression and Anxiety in Twitter (ID)," consists of 2,201 labeled texts on train set and 550 labeled texts on test set. Several evaluation criteria such as recall, precision, F1-score, and accuracy were used on an unseen test set to calculate how well the model performed. The results show that the IndoBERT model achieved an overall accuracy of 86% and a 74.75% F1-score in identifying the depressed class, notably achieving a strong precision score of 79.17%. These findings show the potential of IndoBERT fine-tuning for Indonesian text-based depression-related classification while highlighting the need for further improvements and validation.
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