政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/137675

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/137675

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文筆數/總筆數 : 113324/144300 (79%)
造訪人次 : 51116026 線上人數 : 816

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜尋範圍

查詢小技巧：

您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果

若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料

進階搜尋

主頁 ‧ 登入 ‧ 上傳 ‧ 說明 ‧ 關於政大典藏 ‧ 管理

到手機版

政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/137675

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/137675

題名:	多次 LDA 主題模型及文本探勘應用於多種中文語料 Multiple LDA of Topic Modeling and Text Mining Used in Chinese Corpora
作者:	陳瑀芋 Chen, Yu-Yu
貢獻者:	劉昭麟 Liu, Chao-Lin 陳瑀芋 Chen, Yu-Yu
關鍵詞:	主題模型文本探勘 Topic Modeling LDA
日期:	2021
上傳時間:	2021-11-01 12:00:09 (UTC+8)
摘要:	現今的文本資料數量越來越大，文字探勘分析也越趨重要。社會科學學者需要從大量資料中擷取資訊，並從大量文本中執行相對應的研究，因而衍伸出數位人文研究領域。本研究主要目的為藉由主題模型來找出文本內主要由哪一些主題構成；並對有興趣的主題之下的資訊進行文字探勘，並提供視覺化結果，從而幫助人文學者有效率得進行相關領域的研究。針對主題模型，我們提出了多次LDA的方法，亦針對不同語料，嘗試並比較不同主題模型，結果顯示多次LDA有效提升了主題模型的效果。本研究以輔助社會科學學者研究為出發點，藉由比較不同主題分析模型，使用者得以選擇效果較佳的模型並快速得知文本內的主要主題。另外使用者可以將這些有興趣的文本，進一步進行相關分析，減少學者閱讀大量文本所需耗費之時間。我們利用《人民大學期刊資料庫》、《維基百科》作為實驗與測試的中文語料，且將分析後的結果提供給學者，作為分析詮釋的參考資訊與佐證依據。 Nowadays, the number of textual materials is getting larger and larger, and text exploration and analysis are becoming more and more important. Social science scholars need to extract information from a large amount of data and perform corresponding research from a large amount of text, thus extending the field of Digital Humanities research. The main purpose of this research is to find out which topics are mainly composed of the text through topic modeling; to conduct text mining of the information under the topics of interest, and to provide visualization results, so as to help the humanities scholars to efficiently conduct research in related fields. This research starts from assisting the research of social science scholars. By comparing different topic analysis models, users can choose the best model and quickly learn the main topics in the text. In addition, users can further analyze these interesting texts, reducing the time it takes for scholars to read a large number of texts. For topic models, we proposed the multiple LDA methods. We also tried and compared different topic models with different corpora. The result shows that multiple LDA effectively improved the effect of topic models. We use "Renmin University Journal Database" and "Wikipedia" as the Chinese corpus for experiments and tests, and provide the analysis results to scholars as reference information and supporting evidence for analysis and interpretation.
參考文獻:	[1] 金觀濤、邱偉雲、梁穎誼、陳柏聿、沈錳坤、及劉青峰。觀念群變化的數位人文研究-以《新青年》為例，2014 第五屆數位典藏與數位人文國際研討會，臺灣，2014。   [2] 金觀濤、邱偉雲及劉昭麟。「共現」詞頻分析及其運用——以「華人」觀念起源為例。數位人文要義：尋找類型與軌跡，141-170，國立台灣大學出版中心。 [3] 劉昭麟、金觀濤、劉青峰、邱偉雲、及姚育松。自然語言處理技術於中文史學文獻分析之初步應用，2011 第三屆數位典藏與數位人文國際研討會論文集，151-168，臺灣，2011。 [4] David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research, 3(Jan):993–1022. [5] Bastian M., Heymann S., Jacomy M. 2009. Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media. [6] Di Jiang, Yuanfeng Song, Rongzhong Lian, Siqi Bao, Jinhua Peng and Huang He and Hua Wu. 2018. Familia: A Configurable Topic Modeling Framework for Industrial Text Engineering. arXiv: 1808.03733. [7] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). [8] Leland McInnes, John Healy, James Melville. 2020. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426 [9] McInnes, Leland and Healy, John and Astels, Steve. 2017. hdbscan: Hierarchical density based clustering. The Journal of Open Source Software. 2. 10.21105/joss.00205 [10] Maarten Grootendorst, & Nils Reimers. (2021). MaartenGr/BERTopic: v0.9.1 (v0.9.1). Zenodo. https://doi.org/10.5281/zenodo.5353198 [11] Newman, David & Lau, Jey & Grieser, Karl & Baldwin, Timothy. 2010. Automatic Evaluation of Topic Coherence.. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL. 100-108. [12] Röder Michael, Both Andreas and Hinneburg Alexander. 2015. Exploring the Space of Topic Coherence Measures. WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining. 399-408. 10.1145/2684822.2685324. [13] Sievert, Carson and Shirley, Kenneth. 2014. LDAvis: A method for visualizing and interpreting topics. In Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces. [14] 中國人民大學複印報刊資料 https://ipub.exuezhe.com/index.html。 [15] WikipediaAPI https://www.mediawiki.org/wiki/API:Main_page [16] 人民日報 http://www.people.com.cn/ [17] 結巴中文分詞 https://github.com/fxsjy/jieba。 [18] OpenCC https://github.com/BYVoid/OpenCC [19] 搜狗網 https://pinyin.sogou.com/dict/ [20] Familia https://github.com/baidu/Familia [21] Flair https://github.com/flairNLP/flair [22] HuggingFace https://huggingface.co/ [23]中文情感分析庫 https://github.com/thunderhit/cnsenti
描述:	碩士國立政治大學資訊科學系 108753144
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0108753144
資料類型:	thesis
DOI:	10.6814/NCCU202101663
顯示於類別:	[資訊科學系] 學位論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
314401.pdf		7031Kb	Adobe PDF2	0	檢視/開啟

在政大典藏中所有的資料項目都受到原著作權保護.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

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