National Chung Cheng University Institutional Repository:Item A095B0000Q/514
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 888/888 (100%)
造访人次 : 13688864      在线人数 : 274
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻
    主页登入上传说明关于CCUR管理 到手机版


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ccur.lib.ccu.edu.tw/handle/A095B0000Q/514


    题名: 利用關鍵字熱門程度預測學術期刊論文影響力;Predicting the Impact of Academic Journal Papers by Keyword Popularity
    作者: 蔡承芳;CAI, CHENG-FANG
    贡献者: 資訊管理系研究所
    关键词: 關鍵字熱門程度;潛藏狄利克里分配;Keyword Popularity;Latent Dirichlet Allocation
    日期: 2017
    上传时间: 2019-07-17
    出版者: 資訊管理系研究所
    摘要: 隨著網際網路逐漸蓬勃發展,現今已有許多資訊在網際網路上公開,而人們越來越依賴在網路上的各種資訊,在各領域學術期刊中,研究學者們會隨著時間及議題潮流,關注所要研究的議題,同時也會追求新穎的科技及研究方法來進行實驗,以提高實驗結果準確度。本研究主要運用期刊(Journal)、第一作者(Fisrt Author)及關鍵字熱門程度(Keyword Popularity)來預測期刊論文影響力(Journal Paper Impact),其中,會將關鍵字相關指標數據與潛藏狄利克里分配(Latent Dirichlet Allocation, LDA)的主題機率進行搭配,產生期刊論文的各項關鍵字熱門程度,再運用資料探勘軟體Weka的決策樹(Tree)及函式分類(Functions),前者以C4.5演算法(C4.5 Algorithm, J48),後者分別以羅吉斯回歸(Logistic Regression, Logistic)、支援向量機(Support Vector Machine, SMO)、類神經網路(Artificial Neural Network, MultilayerPerceptron),共四種分類技術來建樣預測模型,進而探討商管領域不同類別對於關鍵字熱門程度的影響程度,以及探討關鍵字熱門程度因子與其他相關因子(期刊、作者)對於該領域的期刊論文影響程度。
    With the Internet gradually flourish, there are a lot of information published on the Internet now, and then people are deeply dependent on the variety of information. In the academic journals in the various fields, researchers will follow the trend of time and topics, and focus on the topics which they would like to experiment. At the same time, they also pursue innovative technologies and new research methologies to experiment and improve the accuracy of experimental results. This study mainly uses the journal, the first author and the keyword popularity to predict the journal paper impact. It takes the related keywords information are meatched with the topic probability of Latent Dirichlet Allocation (LDA), and then it calculates the keyword popularity of each paper. This study uses tree and functions of the data mining software – Weka, there are four classification techniques to build predictive models, the former is C4.5 Algorithm (J48); the later are Logistic Regression (Logistic), Support Vector Machine (SMO), and Artificial Neural Network (MultilayerPerceptron), and then this study explores the different categories of the impact on the keyword popularity in the business and management field, and also explores the keyword popularity factors and other related factors (journal and author) of the impact on the journal paper impact in this field.
    显示于类别:[資訊管理系研究所] 學位論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    index.html0KbHTML382检视/开启


    在CCUR中所有的数据项都受到原著作权保护.

    TAIR相关文章

    版權聲明 © 國立中正大學圖書館網頁內容著作權屬國立中正大學圖書館

    隱私權及資訊安全政策

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈