Classifying aggravation status of COVID-19 event from short-text using CNN

Ekasari Nugraheni, Purnomo Husnul Khotimah, Andria Arisal, Andri Fachrur Rozie, Dianadewi Riswantini, Ayu Purwarianti
2020 2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)  
COVID-19 pandemic is a new precedent that has changed many aspects of human life. With the uncertainty of vaccine availability, stakeholders are required to track the dynamics of COVID-19 events to prepare the necessary response. One sub-task in tracking the dynamics of an event is to identify the aggravation status of the event (i.e., whether an event is worsening or getting better). We experimented with convolutional neural network (CNN) models to classify the status of COVID-19 aggravation
more » ... atus from a short text. CNN without one hot encoding prevailed. Furthermore, we conduct tuning to achieve better performance of CNN. The highest performance was achieved by tuning some of the configuration parameters. As the final result, the model performed at best (accuracy = 87.585% and F1-score = 76%) when using 80 nodes, SGD optimizer, lr = 0.1, and momentum = 0.9.
doi:10.1109/icramet51080.2020.9298674 fatcat:jlfo6v5go5fjxou3cn67wjq5jy