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研究生： 廖彥婷
Liaw, Yen-Ting
論文名稱： 潛在類別分析於文字探勘之應用
Applying Latent Class Analysis on Text Mining
指導教授： 江振東
口試委員: 蔡政安
薛慧敏
江振東
學位類別： 碩士
Master
系所名稱： 商學院 - 統計學系
Department of Statistics
論文出版年： 2018
畢業學年度： 106
語文別： 英文
論文頁數： 46
中文關鍵詞： 分類 、潛在類分析 、文字探勘 、相似性檢測
外文關鍵詞： Classification, Latent class analysis, Similarity detection, Text mining
DOI URL： http://doi.org/10.6814/THE.NCCU.STAT.004.2018.B03
相關次數： 點閱：162  下載：7 
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    • 現今網路的使用已經成為主流，因此在網站上擁有大量的文字信息。文字探勘也因此成為一種流行的資料分析方法。潛在類別分析(Latent Class Analysis)是一常用於社會科學的分析方法來尋找潛藏於資料背後的潛在類別。在本文中，我們應用潛在類別分析來評估此分析方法應用於文字探勘的可行性。本文中針對兩個案例進行論證和研究，一個是比較“水滸傳”和“三國演義”的相似性檢測，另一個則是針對新聞文章的分類問題來尋找關鍵詞並據此提供結論和建議。

    There is a large amount of information on the website that is in text form, and due to the increment of internet usage, text mining has become a popular method for information retrieval. In this paper, we apply Latent Class Analysis (LCA), a technique that is often used in social sciences to reveal underlying latent classes, on text mining and check whether it is an appropriate method on this regard. Two study cases are demonstrated, one is similarity detection that compare two novels, Water Margin and Romance of Three Kingdom, and the other is using classification that classify the categories for news articles to find important keywords. Conclusions and suggestions are provided.

    Table Directory 4
    Figure Directory 5
    1. Introduction 6
    2. Literature review 7
    2.1 Latent Class Analysis 8
    3. Case Study 1: Similarity Detection (Water Margin and Romance of Three Kingdom) 10
    3.1 Data 10
    3.2 Data preprocessing 12
    3.3 Text mining methodology 15
    3.4 Results and discussion 22
    4. Case Study 2: Keyword Extraction (News Articles from chinatimes.com) 23
    4.1 Data 23
    4.2 Data Preprocessing 25
    4.3 Data Analysis 28
    4.4 Result and Discussion 32
    5. Conclusion 35
    Appendix 1: The selected word for feature candidates 37
    Appendix 2: The selected word for 1-word/ 2-word/ 3-word keyword 38
    Appendix 3: The selected keywords for each LCA 39
    Appendix 4: Code for Case Study 1 40
    Appendix 5: Code for Case Study 2 42
    Reference 45
    Mandarin Reference 46

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