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政大機構典藏 > 商學院 > 統計學系 > 學位論文 > Item 140.119/153362

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题名:	臺灣碩博士論文之文字分析—以商業及管理學門摘要為例 Text Analysis of Master’s and Doctoral Theses in Taiwan: A Study on Abstracts in the Field of Business and Administration
作者:	劉貞莉 Liu, Chen-Li
贡献者:	陳怡如余清祥 Chen, Yi-Ju Yue, Ching-Syang 劉貞莉 Liu, Chen-Li
关键词:	文字分析中文斷詞探索性資料分析文本分類關聯性分析 Text Analysis Word Segmentation Exploratory Data Analysis Classification Association Analysis
日期:	2024
上传时间:	2024-09-04 14:55:57 (UTC+8)
摘要:	自從人類發明文字，文字一直是人類傳遞知識、故事和情感的重要工具，藉由文字分析可以探索各時期的文化及科技等發展、社會特色及變遷軌跡，並能鉅細靡遺地發掘其中的關鍵因素。摘要則是文章、書籍的縮影，通常可在摘要的文字及其內容一窺全文的關鍵，以學術論文為例，讀者應能從摘要知道文章的研究目的、結論、重要啟發等要素。本研究以107至109學年度臺灣商業及管理（簡稱商管）學門的碩博士論文摘要為研究對象，除了整理論文的用字等寫作風格外，同時也嘗試使用群集分析等工具，剖析摘要三個單元的文字風格，比較商管各學類論文的特色，協助讀者撰寫及研讀商管學門的論文。由於現代中文主要以白話文為主，通常以兩個字及以上組成的詞彙為基本單位，分析白話文時會先經過斷詞處理，取得更接近文意的重要詞彙。本研究將先探討兩種斷詞套件：Jieba和CKIP，從執行時間、詞彙數量、詞彙比例、詞彙種類與斷詞精確度等面向進行比較，提供使用者分析中文的參考。而摘要的文字分析主要從探索性資料分析著手，以人工標示將摘要分成「動機目的」、「方法素材」與「結論建議」三個單元，並根據斷詞結果的常見詞彙、字詞多樣性與共現詞叢等角度，探索商管論文的十個學類之寫作風格。資料分析顯示，CKIP斷詞結果能捕捉到臺灣碩博士商管學門論文摘要的慣用詞語，整體結果較符合本研究的期望。摘要三個單元之間的特徵與格式相當明顯，商管學門的十大學類可分為三大集群：醫管、會計、以及其他學類。另外，以各集群與各單元的常見詞彙與共現詞叢作為解釋變數，代入分類模型能有效地區隔商管學門的三個集群、摘要三個單元。 Writing has been a crucial tool for humans to exchange knowledge and express emotions. Through text analysis, we can explore the cultural and technological developments in various eras and understand social characteristics and changes. An abstract serves as the epitome of an article or book, often providing key insights of the full text. For example, readers are usually able to discern the research objectives, conclusions, and significant insights from the abstract of an academic paper. This paper studies the abstracts of master’s and doctoral theses in the field of business and administration (BA) in Taiwan between 2018 and 2020, using cluster analysis to dissect the textual styles of the three sections of the abstracts. The goal is to compare the characteristics of theses across various BA disciplines and to assist readers in writing and understanding BA academic papers. Modern Chinese writing typically consists of phrases (two or more words) as a basic unit, and thus word segmentation is the first step in analyzing Chinese text. We evaluate two word segmentation tools: Jieba and CKIP, and compare them in terms of execution time and segmentation accuracy to provide references for users analyzing Chinese text. For the study of textual style, we apply tools in exploratory data analysis and examine common terms, word diversity, and co-occurrence terms in abstracts, based on the word segmentation results. Note that the abstracts can be divided into three sections: Motivations & Purposes, Methods & Materials, and Conclusions & Suggestions. The analysis results show that the CKIP tool can capture the commonly used terms in master’s and doctoral thesis abstracts in Taiwan, aligning better with the expectations of this study. Additionally, by using the common terms and co-occurrence terms as explanatory variables in classification models, we can effectively distinguish between the three clusters of BA disciplines and the three sections of the abstracts.
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描述:	碩士國立政治大學統計學系 111354014
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0111354014
数据类型:	thesis
显示于类别:	[統計學系] 學位論文

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