DepressionOne@LT-EDI-ACL2022: Using Machine Learning with SMOTE and Random UnderSampling to Detect Signs of Depression on Social Media Text.

Suman Dowlagar, Radhika Mamidi


Abstract
Depression is a common and serious medical illness that negatively affects how you feel, the way you think, and how you act. Detecting depression is essential as it must be treated early to avoid painful consequences. Nowadays, people are broadcasting how they feel via posts and comments. Using social media, we can extract many comments related to depression and use NLP techniques to train and detect depression. This work presents the submission of the DepressionOne team at LT-EDI-2022 for the shared task, detecting signs of depression from social media text. The depression data is small and unbalanced. Thus, we have used oversampling and undersampling methods such as SMOTE and RandomUnderSampler to represent the data. Later, we used machine learning methods to train and detect the signs of depression.
Anthology ID:
2022.ltedi-1.45
Volume:
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Bharathi Raja Chakravarthi, B Bharathi, John P McCrae, Manel Zarrouk, Kalika Bali, Paul Buitelaar
Venue:
LTEDI
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
301–305
Language:
URL:
https://aclanthology.org/2022.ltedi-1.45
DOI:
10.18653/v1/2022.ltedi-1.45
Bibkey:
Cite (ACL):
Suman Dowlagar and Radhika Mamidi. 2022. DepressionOne@LT-EDI-ACL2022: Using Machine Learning with SMOTE and Random UnderSampling to Detect Signs of Depression on Social Media Text.. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 301–305, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
DepressionOne@LT-EDI-ACL2022: Using Machine Learning with SMOTE and Random UnderSampling to Detect Signs of Depression on Social Media Text. (Dowlagar & Mamidi, LTEDI 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.ltedi-1.45.pdf