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

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政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/137675

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/137675

Title:	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
Authors:	陳瑀芋 Chen, Yu-Yu
Contributors:	劉昭麟 Liu, Chao-Lin 陳瑀芋 Chen, Yu-Yu
Keywords:	主題模型文本探勘 Topic Modeling LDA
Date:	2021
Issue Date:	2021-11-01 12:00:09 (UTC+8)
Abstract:	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.
Reference:	[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
Description:	碩士國立政治大學資訊科學系 108753144
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0108753144
Data Type:	thesis
DOI:	10.6814/NCCU202101663
Appears in Collections:	[Department of Computer Science ] Theses

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