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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65136


    Title: 推薦期刊文章至適合學科類別之研究;Recommending Subject Categories for Journal articles
    Authors: 王玉峯;Wang,Yu-fang
    Contributors: 企業管理學系在職專班
    Keywords: 天真貝氏法;巨量資料;文字探勘;Naïve Bayes;Big Data;text mining
    Date: 2014-07-21
    Issue Date: 2014-10-15 14:41:37 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 年輕學者在投稿時期刊文章,時常會有誤判學科類別(Subject Categories)的問題出現。本研究嘗試以英文期刊文章標題(Journal Title)來進行分析,探討期刊文章適合投稿的學科類別之間的吻合關係。在過去研究中不曾僅使用文章標題之斷詞後文字(Text)做為類別分類的基礎,此外當面臨相當龐大的資料量和類別廣度時,為瞭解探究其分類結果,所用方法包含:每篇文章標題之斷詞後各個文字出現的文字、次數和學科類別集合,以及天真貝氏分類法(Naïve Bayes)。所獲得預測命中與否的結果準確度分別有兩種:一種為概括文章命中率(Rough Hitting Ratio, RHR)67.24%,另一種為精實學科類別命中率(Precise Hitting Ratio, PHR)38.34%。;With the proliferation of academic journals, a common issue faced by young scholars or researchers who wish to tread into the field of cross disciplines is to locate suitable categories and journals to submit their works. To lessen the severity of the issue, this research proposed a Naïve Bayes Classification method to recommend subject categories for a manuscript by analyzing the title words.
    The challenging of this study came from the huge amount of data. By limiting the subject categories to the areas where NCU faculty members have published in the past three years, we got 64 categories and 199 journals. The number of articles in these journals are 224,870 The data that are used to build the classification model consists of 171,625 records and the testing data have 53,245 records. With intensive coding, the study is able to come out with a system to handle the job with reasonable performance. The Hit ratios are 67.24% and 38.34% for Rough Hitting Ratio (RHR) and Precise Hitting Ratio (PHR), respectively.
    Appears in Collections:[企業管理學系碩士在職專班] 博碩士論文

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