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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
Chen, Yu-Yu
Keywords: 主題模型
Topic Modeling
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。
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.
[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。
[15] WikipediaAPI
[16] 人民日報
[17] 結巴中文分詞。
[18] OpenCC
[19] 搜狗網
[20] Familia
[21] Flair
[22] HuggingFace
Description: 碩士
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Data Type: thesis
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