Shayona@SMM4H23: COVID-19 Self diagnosis classification using BERT and LightGBM models

• URL: http://arxiv.org/abs/2401.02158v1
• Date: Thu, 4 Jan 2024 09:13:18 GMT
• Title: Shayona@SMM4H23: COVID-19 Self diagnosis classification using BERT and LightGBM models
• Authors: Rushi Chavda, Darshan Makwana, Vraj Patel, Anupam Shukla
• Abstract summary: This paper describes approaches and results for shared Task 1 and 4 of SMMH4-23 by Team Shayona. Our team has achieved the highest f1-score 0.94 in Task-1 among all participants.
• Score: 1.5566524830295307
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper describes approaches and results for shared Task 1 and 4 of SMMH4-23 by Team Shayona. Shared Task-1 was binary classification of english tweets self-reporting a COVID-19 diagnosis, and Shared Task-4 was Binary classification of English Reddit posts self-reporting a social anxiety disorder diagnosis. Our team has achieved the highest f1-score 0.94 in Task-1 among all participants. We have leveraged the Transformer model (BERT) in combination with the LightGBM model for both tasks.

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