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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/83811

    Title: 機器學習分類防疫新聞;A Study on Text Classification for epidemic prevention News
    Authors: 劉冠麟;Liu, Kuan-Lin
    Contributors: 通訊工程學系在職專班
    Keywords: 機器學習;文本分類;新聞分類;Machine learning;Text Classification;News Classification
    Date: 2020-07-23
    Issue Date: 2020-09-02 17:09:26 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 2019年12月於中國大陸湖北武漢地區,發現新型冠狀病毒,隨後在2020年初迅速蔓延至全球,逐漸造成全球性的大瘟疫,被多個國際組織及新聞媒體形容是 多個國際組織及傳媒形容為自第二次世界大戰以來全球面臨的最嚴峻危機。截至2020年5月,全球已有220多個國家和地區累計報告逾471萬名確診病例,逾35萬名患者死亡。
    ;The COVID-19 pandemic, also known as the coronavirus pandemic, is an ongoing pandemic of coronavirus disease 2019 (COVID‑19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2). The outbreak was first identified in Wuhan, China , in December 2019. The World Health Organization declared the outbreak a Public Health Emergency of International Concern on 30 January, and a pandemic on 11 March. As of May 2020, more than 4.71 million cases of COVID-19 have been reported in more than 188 countries and territories, resulting in more than 315,000 deaths. More than 1.73 million people have recovered from the virus. this paper is based on the global pandemic of COVID‑19. About half of the daily news reports in Taiwan are related to COVID‑19 or epidemic prevention knowledge. This thesis studies different classification methods for the COVID-19 epidemic prevention news. Based on practical news data collected from web pages, our simulation results show that the decision tree method achieves the best
    classification result with an accuracy of 0.927.
    Appears in Collections:[通訊工程學系碩士在職專班 ] 博碩士論文

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