Multi-Label Classification of COVID-Tweets Using Large Language Models

December 17, 2023 · Entered Twilight · 🏛 Fire

Repo contents: README.md, calculate_ranks.py, cosine_calc.py, cosine_calc_run.sh, data_generator.py, evaluate.py, experiments, gen_all_embeds.sh, gen_all_ranks.sh, generate_embeddings.py, ir_metrics.py, ir_metrics_run.sh, map.py, map_run.sh, metrics.py, model, ndcg.py, run_ndcg.sh, run_several.sh, train.py, utils.py

Authors Aniket Deroy, Subhankar Maity arXiv ID 2312.10748 Category cs.CL: Computation & Language Cross-listed cs.SI Citations 5 Venue Fire Repository https://github.com/anonmous1981/AISOME Last Checked 3 months ago

Abstract

Vaccination is important to minimize the risk and spread of various diseases. In recent years, vaccination has been a key step in countering the COVID-19 pandemic. However, many people are skeptical about the use of vaccines for various reasons, including the politics involved, the potential side effects of vaccines, etc. The goal in this task is to build an effective multi-label classifier to label a social media post (particularly, a tweet) according to the specific concern(s) towards vaccines as expressed by the author of the post. We tried three different models-(a) Supervised BERT-large-uncased, (b) Supervised HateXplain model, and (c) Zero-Shot GPT-3.5 Turbo model. The Supervised BERT-large-uncased model performed best in our case. We achieved a macro-F1 score of 0.66, a Jaccard similarity score of 0.66, and received the sixth rank among other submissions. Code is available at-https://github.com/anonmous1981/AISOME