本文選題：文獻(xiàn)計量 + BibExcel　； 參考：《華中師范大學(xué)》2017年碩士論文

【摘要】：進(jìn)入信息化和大數(shù)據(jù)時代,各個學(xué)科研究對數(shù)據(jù)庫的依賴進(jìn)一步加深。雖然國內(nèi)外建立了眾多大型的、綜合性的數(shù)據(jù)庫,國內(nèi)數(shù)據(jù)庫如CSSCI、CNKI、萬方、維普等,國外數(shù)據(jù)庫如SCI、SSCI、Scopus、Derwent等,但由于這些綜合性數(shù)據(jù)庫涵蓋各類學(xué)科,數(shù)據(jù)庫存儲的數(shù)據(jù)量巨大,致使各學(xué)科研究者在檢索某一特定研究主題時,往往會得到成千上萬條檢索結(jié)果,在這些檢索結(jié)果中,存在許多不相關(guān)信息。因此,如何剔除這些雜質(zhì)信息獲得滿足自己需求的關(guān)鍵性、價值高的信息,成為人們?nèi)找骊P(guān)注的焦點問題。針對該問題,本文以“Big Data”為實例,以SCI-EXPANDED、SSCI、AHCI中相關(guān)文獻(xiàn)數(shù)據(jù)為實證數(shù)據(jù)來源,對文獻(xiàn)計量常用軟件進(jìn)行功能方面的分類及比較,尤其是在對同一主題數(shù)據(jù)進(jìn)行分析時,針對具有相似功能的文獻(xiàn)計量常用軟件在分析方法、分析過程及分析結(jié)果等共性與個性方面進(jìn)行深度解析和比較。本文研究的必要性在于:(1)對分析文獻(xiàn)計量學(xué)中常用的軟件進(jìn)行充分比較,總結(jié)各自軟件的優(yōu)缺點及用途;(2)針對各自優(yōu)缺點提出個性化的優(yōu)化建議,為各學(xué)科用戶提供參考,為開發(fā)人員對軟件進(jìn)行優(yōu)化提供建議。全文共分為六個部分:第一部分是緒論。該部分首先介紹了本文的研究背景、研究意義,通過對目前國內(nèi)外現(xiàn)有的相關(guān)文獻(xiàn)進(jìn)行整理、歸納和分析,提出本文的研究思路、研究技術(shù)路線,對研究內(nèi)容與主要創(chuàng)新點進(jìn)行說明。第二部分介紹了文獻(xiàn)計量相關(guān)理論。該部分對文獻(xiàn)計量相關(guān)理論的概念和發(fā)展歷史進(jìn)行概述,介紹了文獻(xiàn)計量相關(guān)理論中詞頻分析法、共現(xiàn)分析等基本方法與基本原理;并充分論述了文獻(xiàn)計量學(xué)與社會網(wǎng)絡(luò)分析法之間的聯(lián)系。第三部分是常用軟件的功能分類及比較研究。該章節(jié)主要內(nèi)容是根據(jù)文獻(xiàn)計量軟件功能對常用軟件進(jìn)行分類,并分別進(jìn)行對比分析。第四部分是實證分析部分,也是本文的核心章節(jié)。該章節(jié)以“Big Data”為實證對象,通過對Web of Science數(shù)據(jù)庫進(jìn)行檢索,獲取“Big Data”相關(guān)數(shù)據(jù),從功能、提取過程和優(yōu)缺點等方面對文獻(xiàn)計量常用軟件進(jìn)行對比分析。第五部分和第六部分是本文的結(jié)論與建議以及總結(jié)。這兩部分分別對本文的所提出的改進(jìn)意見和建議、研究內(nèi)容、研究過程以及研究不足進(jìn)行總結(jié)。
[Abstract]:In the era of information and big data, the dependence of each subject on database has been deepened. Although many large and comprehensive databases have been established at home and abroad, domestic databases such as CSSCI CNKI, Wanfang, Wiper, and foreign databases such as SCI SSCI Scopus Derwent, etc., but because these comprehensive databases cover all kinds of disciplines, the amount of data stored in the databases is huge. As a result, researchers in various disciplines often get thousands of retrieval results when retrieving a particular research topic. In these retrieval results, there is a lot of irrelevant information. Therefore, how to eliminate these impurity information to obtain the key and high value information to meet their needs has become the focus of increasing attention. In order to solve this problem, taking "Big Data" as an example and taking the relevant literature data in SCI-EXPANDEDU SSCI as the source of empirical data, this paper classifies and compares the functional aspects of the commonly used software of literature measurement, especially in the analysis of the data on the same subject. The common software of literature metrology with similar functions is analyzed and compared in the aspects of analysis methods, analysis process and analysis results in the aspects of commonness and individuality. The necessity of this study lies in: (1) making a full comparison of the commonly used software in bibliometrics, and summarizing the advantages and disadvantages and uses of their respective software. Provide advice to developers for software optimization. The paper is divided into six parts: the first part is the introduction. This part first introduces the research background and significance of this paper, through the existing literature at home and abroad to sort out, induction and analysis, put forward the research ideas, research technology route, The research content and main innovation points are explained. The second part introduces the theory of literature measurement. In this part, the concept and development history of literature metrology theory are summarized, and the basic methods and principles of word frequency analysis and co-occurrence analysis are introduced. The relationship between bibliometrics and social network analysis is discussed. The third part is the functional classification and comparative study of common software. The main content of this chapter is to classify the commonly used software according to the function of the literature measurement software, and to carry on the contrast analysis respectively. The fourth part is the empirical analysis part, is also the core chapter of this paper. This chapter takes "Big Data" as the empirical object, through the Web of Science database retrieval, obtains "Big Data" the related data, from the function, the extraction process, the advantage and disadvantage and so on aspect carries on the contrast analysis to the literature metrology commonly used software. The fifth and sixth parts are the conclusions and suggestions of this paper. These two parts summarize the suggestions, research contents, research process and deficiencies of this paper.
【學(xué)位授予單位】：華中師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP319;G353.1

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