Data Augmentation for Mental Health Classification on Social Media

Gunjan Ansari, Muskan Garg, Chandni Saxena


Abstract
The mental disorder of online users is determined using social media posts. The major challenge in this domain is to avail the ethical clearance for using the user-generated text on social media platforms. Academic researchers identified the problem of insufficient and unlabeled data for mental health classification. To handle this issue, we have studied the effect of data augmentation techniques on domain-specific user-generated text for mental health classification. Among the existing well-established data augmentation techniques, we have identified Easy Data Augmentation (EDA), conditional BERT, and Back-Translation (BT) as the potential techniques for generating additional text to improve the performance of classifiers. Further, three different classifiers- Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR) are employed for analyzing the impact of data augmentation on two publicly available social media datasets. The experimental results show significant improvements in classifiers’ performance when trained on the augmented data.
Anthology ID:
2021.icon-main.19
Volume:
Proceedings of the 18th International Conference on Natural Language Processing (ICON)
Month:
December
Year:
2021
Address:
National Institute of Technology Silchar, Silchar, India
Editors:
Sivaji Bandyopadhyay, Sobha Lalitha Devi, Pushpak Bhattacharyya
Venue:
ICON
SIG:
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
152–161
Language:
URL:
https://aclanthology.org/2021.icon-main.19
DOI:
Bibkey:
Cite (ACL):
Gunjan Ansari, Muskan Garg, and Chandni Saxena. 2021. Data Augmentation for Mental Health Classification on Social Media. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 152–161, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).
Cite (Informal):
Data Augmentation for Mental Health Classification on Social Media (Ansari et al., ICON 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.icon-main.19.pdf
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