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Dr. Hamada Ali Mohamed Ali Nayel :: Publications: |
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| Title: | BFCI at# SMM4H 2023: Integration of Machine Learning and TF-IDF for Covid-19 Tweets Analysis |
| Authors: | Hamada Nayel; Nsrin Ashraf; Mohamed Aldawsari |
| Year: | 2023 |
| Keywords: | Covid-19 Tweets Analysis; Fake News Detection; SMM4H |
| Journal: | medRxiv |
| Volume: | Not Available |
| Issue: | Not Available |
| Pages: | Not Available |
| Publisher: | Cold Spring Harbor Laboratory Press |
| Local/International: | International |
| Paper Link: | |
| Full paper | Hamada Ali Mohamed Ali Nayel_2023.11.18.23297862v2.full.pdf |
| Supplementary materials | Not Available |
| Abstract: |
Extracting information from texts generated by users of social media platforms becomes a crucial task. In this paper, we describe the systems submitted to the SMM4H shared tasks 1 and 2. The aims of these two tasks are binary and multi-class classification of English tweets. We developed a machine learning-based model integrated with TF-IDF as a feature extraction approach. Four classification algorithms have been implemented namely, support vector machines, passive-aggressive classifier, multi-layer perceptron and random forest. For task 1, the passive-aggressive classifier reported f1-score of 63.7%. For task 2, multi-layer perceptron reported f1-score of 71.4%. |
