| Title |
Automatic Extraction of Keywords for COVID-19 Cases Increment Detection |
| Authors |
정현빈(Hyeonbin Jung) ; 장길진(Gil-Jin Jang) |
| DOI |
https://doi.org/10.5573/ieie.2023.60.9.39 |
| Keywords |
COVID-19; Term frequency (TF); Keyword extraction; Confirmed cases detection |
| Abstract |
This paper proposes an automatic keyword extraction and binary classification method for increment detection of COVID-19 confirmed cases. The conventional keyword extraction methods use term frequency (TF) and inverse document frequency (IDF) from all the documents, so the resultant keywords reflect general word distributions rather than a specfic event. The proposed extraction method provides keywords closely related to the disease by the given statistics of the COVID-19 confirmed cases, and uses a binary classifier implemented by random forest. Experimental results show that the AUC (area under the curve) metric is improved by 13% compared to the conventional methods. |