政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141743

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141743

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题名:	主題分析方法在經濟文獻學上的應用：隱含狄利克雷分配與代理人基計算經濟學 Topic Analysis in the Automatic Organization of Economic Literature: The Case of Agent-Based Computational Economics with the Use of Latent Dirichlet Allocation
作者:	胡瑞軒 Hu, Ruei-Xsuan
贡献者:	陳樹衡 Chen, Shu-Heng 胡瑞軒 Hu, Ruei-Xsuan
关键词:	代理人基建模非監督學習詞彙頻率-逆文檔頻率文字雲自然語言處理主題一致性主題相似度 Agent-Based Modeling Unsupervised Learning TF-IDF Wordcloud NLP Topic coherence Topic similarity
日期:	2022
上传时间:	2022-09-02 15:26:59 (UTC+8)
摘要:	本文將多個期刊的代理人基建模(Agent-Based Modeling, ABM) 的論文用主題模型中的隱含狄利克雷分配(Latent Dirichlet Allocation, LDA) 進行分類，接著用詞彙頻率-逆文檔頻率(Term Frequency-Inverse Document Frequency, TF-IDF) 與文字雲分別找出與該主題相關卻被過濾掉的詞彙以及主題之間的相同詞彙並且對於每個主題所屬的期刊進行分類並分析主題隨時間的變化。最後，主題相似度、主題排名與主題一致性分析結果顯示每個主題的重疊度不大，主題解釋比例與一致性都很高。本文有別於過往研究，進行多個期刊的分析以及分類之後的評估，主題相似度、主題排名與主題一致性評估方式顯示隱含狄利克雷分配模型能有效地量化具體的方式將文檔分類，且比人為的分類方式降低更多時間成本與資料複雜度。 In this paper, we classify Agent-Based Modeling (ABM) papers in multiple journals with Latent Dirichlet Allocation (LDA) in topic model. By applying analyses of TF-IDF algorithm and word cloud, we recollect words related to the topic but filtered out in the first place and gather same words belonging to different topics. Also, we analyze the dynamics of topics in several journals over time. Finally, the results of topic similarity, topic ranking and topic consistency analysis show that each topic has little overlap, and the topic explanation ratio and consistency are high. Different from previous studies, we classify ABM papers in multiply journals and have further evaluations. The evaluation methods of topic similarity, topic ranking and topic consistency show that the implicit Dirichlet allocation model can effectively quantitatively classify documents. And it reduces more time cost and data complexity than artificial classification.
參考文獻:	[1] Ambrosino, A., Cedrini, M., Davis, J. B., Fiori, S. Guerzoni, M., & Nuccio, M. (2018). What topic modeling could reveal about the evolution of economics. Journal of Economic Methodology, 25(4), 329-348. [2] Alexakis, C., Doolig, M., Eleftheriou, K., & Polemis, M. (2020). Textual Machine Learning: An Application to Computational Economics Research. Computational Economics, 57(1), 369-385. [3] Blei, D. M., Jordan, M. I, & Ng, A. Y.. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3(2003), 993-1022. [4] Boyd-Graber, J., Hu, Y., & Mimno, D. (2017). Applications of topic models. Foundations and Trends in Information Retrieval, 11(2-3), 143–296. [5] Hannigan, T. R., Haans, R. F., Vakili, K., Tchalian, H., Glaser, V. L., Wang, M. S., et al. (2019). Topic modeling in management research: rendering new theory from textual data. Academy of Management Annals, 13(2), 586–632. [6] Hofmann, T. (1999). Probabilistic Latent Semantic Analysis. Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI-99), Stockholm, 289-296. [7] Huang, A. H., Lehavy, R., Zang, A. Y., & Zheng, R. (2018). Analyst information discovery and interpretation roles: a topic modeling approach. Management Science, 64(6), 2833-2855. [8] Kao, Y. F., & Venkatachalam, R. (2018). Human and Machine Learning. Computational Economics, 57(4), 889-909. [9] Kumar, A., & Paul, A. (2016). Mastering Text Mining with R. UK：Packt Publishing Ltd. [10] Mimno, D., Leenders, M., McCallum, A., Talley, E., & Wallach, H. M. (2011). Optimizing Semantic Coherence in Topic Models. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, 262-272. [11] Newman, D., Lau, J. H., Grieser, K., & Baldwin, T. (2010). Automatic Evaluation of Topic Coherence. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL, 100-108. [12] Papadimitriou, C. H., Raghavan, P., Tamaki, H., & Vempala, S. (1999). Latent Semantic Indexing: A Probabilistic Analysis. Journal of Computer and System Sciences, 61(2), 217-235. [13] Polyakov, M., Chalak, M., Iftekhar, M. S., Pandit, R., Tapsuwan, S., Zhang, F., & Ma, C. (2017). Authorship, Collaboration, Topics, and Research Gaps in Environmental and Resource Economics 1991–2015. Environmental and Resource Economics volume 71(1), 217-239. [14] Piepenbrink, A., & Nurmammadov, E. (2015). Topics in the literature of transition economies and emerging markets. Scientometrics, 102(3), 2107-2130. [15] Tesfatsion, L. (2021). Agent-Based Computational Economics: Overview and Brief History. Working Paper 21004, Department of Economics, Iowa State University. [16] Tesfatsion, L. (2022, January 1). Agent-Based Computational Economics(ACE). Intro Materials and Research Area Sites. http://www2.econ.iastate.edu/tesfatsi/aapplic.htm
描述:	碩士國立政治大學經濟學系 109258032
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0109258032
数据类型:	thesis
DOI:	10.6814/NCCU202201265
显示于类别:	[經濟學系] 學位論文

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