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Classifying aggravation status of COVID-19 event from short-text using CNN
Nugraheni E.a, Khotimah P.H.a, Arisal A.a, Rozie A.F.a, Riswantini D.a, Purwarianti A.b
a Indonesian Institute of Sciences, Research Center for Informatics, Bandung, Indonesia
b Bandung Institute of Technology, School of Electrical Engineering and Informatics, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 IEEE.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 status 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.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Configuration parameters,F1 scores,Human lives,Optimizers,Short texts,Subtasks[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]CNN,covid,news title,sentiment analysis,short text[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICRAMET51080.2020.9298674[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]