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

arXiv: https://arxiv.org/abs/2401.02158 · HTML: https://mlsanity.com/paper/2401.02158/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 4 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter](https://mlsanity.com/paper/2005.07503/) (2020)
- [An Introductory Survey on Attention Mechanisms in NLP Problems](https://mlsanity.com/paper/1811.05544/) (2018)
- [Detection and Classification of mental illnesses on social media using RoBERTa](https://mlsanity.com/paper/2011.11226/) (2020)

## Abstract (arXiv)

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.