SSN_MLRG1@LT-EDI-ACL2022: Multi-Class Classification using BERT models for Detecting Depression Signs from Social Media Text

Anantharaman, Karun; Angel, S; Sivanaiah, Rajalakshmi; Madhavan, Saritha; Rajendram, Sakaya Milton

doi:10.18653/v1/2022.ltedi-1.44

Cited by 2 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the authors experimented with two ways of diagnosing depression, i.e., by either employing a majority-vote approach or training a hierarchical attention network. Anantharaman et al [49] fine-tuned a BERT model for classifying the signs of depression into three labels, namely, "not depressed," "moderately depressed," and "severely depressed." Similarly, Nilsson and Kovács [50] exploited a BERT model and used abstractive summarization techniques for data augmentation.…”

Section: B Depression Detectionmentioning

confidence: 99%

Calibration of Transformer-Based Models for Identifying Stress and Depression in Social Media

Ilias

Mouzakitis

Askounis

2024

IEEE Trans. Comput. Soc. Syst.

View full text Add to dashboard Cite

Section: B Depression Detectionmentioning

confidence: 99%

Calibration of Transformer-Based Models for Identifying Stress and Depression in Social Media

Ilias

Mouzakitis

Askounis

2024

IEEE Trans. Comput. Soc. Syst.

View full text Add to dashboard Cite

Explainable Deep Learning for Mental Health Detection from English and Arabic Social Media Posts

Kumar,

Kumari,

Pradhan

2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

View full text Add to dashboard Cite

The research communities have recently begun exploring the detection of depression through social media, making it a relatively new development in this field. Some research has been done to identify depressive signs from English social media postings, while little work has been done for the Arabic posts. This paper proposes a BERT and Bi-LSTM pipeline for the identification of depressive signs from Arabic social media posts. A fine-tuned RoBERTa-based model is also proposed for English social media posts for depressive state identification. Along with the proposed model, seven conventional machine learning and eight deep learning models are also explored for the identification of depressive signs from Arabic and English social media posts. The performance of the proposed model is validated on two Arabic datasets and one English dataset. The proposed BERT and Bi-LSTM pipeline achieved state-of-the-art performance with an F 1 -score of 1.00 and 0.82 for two different Arabic datasets, whereas the proposed fine-tuned RoBERTa achieved a F 1 -score of 0.60 which is comparable in identifying depressive sign from English social media posts. The majority of the suggested deep learning models are end-to-end, which necessitates a greater explanation for their success. An explainable AI-based model may enhance decision-making, transparency, and interpretability. Therefore, this research identifies where the suggested system learned well and where it failed in recognition of the depression signs that can help in future developments in the field of depression detection.

show abstract

SSN_MLRG1@LT-EDI-ACL2022: Multi-Class Classification using BERT models for Detecting Depression Signs from Social Media Text

Cited by 2 publications

References 6 publications

Calibration of Transformer-Based Models for Identifying Stress and Depression in Social Media

Calibration of Transformer-Based Models for Identifying Stress and Depression in Social Media

Explainable Deep Learning for Mental Health Detection from English and Arabic Social Media Posts

Contact Info

Product

Resources

About