Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/100499
|
Title: | 應用社會網路分析於易經爻辭之文字特徵觀察 Application of Social Network Analysis For Text Characteristic Observation On I-Ching Line Statements |
Authors: | 李俊澔 Lee, Chun Hao |
Contributors: | 劉吉軒 Liu, Jyi Shane 李俊澔 Lee, Chun Hao |
Keywords: | 易經爻辭 詞頻分析 社會網路分析 資料分析 I Ching Line Statements Word Frequency Analysis Social Network Analysis Data Analysis |
Date: | 2016 |
Issue Date: | 2016-08-22 11:06:55 (UTC+8) |
Abstract: | 隨著資訊技術的進步,各種史料文本的數位化工作已經處理完成,運用資訊技術於史料文本分析的研究日益增加。本研究以詞頻分析與社會網路分析為主軸,對於古代《易經》爻辭的文字進行多元化的觀察,本研究首先以詞頻分析探討《易經》爻辭字詞頻率的觀察,再利用《易經》爻辭位置資訊建構成各個社會網路結構,對每個社會網路結構運算各項社會網路指標數據,最後將實驗結果與過往《易經》爻辭的論點做印證與對照,期望對於《易經》爻辭之分析,有更多元性的客觀研究觀察。本研究提供了一個分析《易經》爻辭的新面向,也可供未來研究者對於其他古文研究作參考。 With advances in information technology, digitization of various historical text has been completed.The study of historical text analysis by using information technology is in-creasing daily.In this paper, we used word frequency analysis and social network analy-sis in the I-Ching line statements.First, we used word frequency analysis in I-Ching line statements,using N-gram and TF-IDF technique analysis word frequency.Second, we constructed social network structure by I-Ching line statements position infor-mation,calculating several social network analysis indicator on each network.We com-pared our experiment results with some existing I-Ching theory, expecting to get more objective results and more diverse analysis for the I-Ching line statements. We not only provided a new perspective to study I-Ching line statements but also expected to help other researchers to study different historical text. |
Reference: | [1] Chen, S.-P., et al., On building a full-text digital library of historical documents, in Asian Digital Libraries. Looking Back 10 Years and Forging New Frontiers. 2007, Springer. p. 49-60. [2] Sturgeon(德龍), D. 中國哲學書電子化計劃(Chinese Text Project). 2011; Available from: http://ctext.org. [3] 項潔、涂豐恩, 導論—什麼是數位人文, in 從保存到創造: 開啟數位人文研究. 2011. p. 9-28. [4] Manning, C.D., P. Raghavan, and H. Schütze, Introduction to information retrieval. Vol. 1. 2008: Cambridge university press Cambridge. [5] Han, J., M. Kamber, and J. Pei, Data mining: concepts and techniques: concepts and techniques. 2011: Elsevier. [6] 金觀濤、邱偉雲、劉昭麟, 「共現」詞頻分析及其運用:以「華人」觀念起源為例, in 數位人文要義 : 尋找類型與軌跡, 項潔, Editor. 2012, 臺灣大學出版中心. p. 141-170. [7] Edmonds, P. Choosing the word most typical in context using a lexical co-occurrence network. in Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics. 1997. Association for Computational Linguistics. [8] Scott, J., Social network analysis. 2012: Sage. [9] 劉吉軒、柯雲娥、張惠真、譚修雯、黃瑞期、甯格致, 以文本分析呈現臺灣海外史料政治思想輪廓, in 數位人文要義 : 尋找類型與軌跡, 項潔, Editor. 2012, 臺灣大學出版中心. p. 83-116. [10] 張善文, 歷代易學要籍解題. 2006, 頂淵文化. [11] 鄭吉雄, 從卦爻辭字義的演繹論《 易傳》 對《 易經》 的詮釋, in 漢學研究. 2006. p. 1-33. [12] 陳伯适, 李道平《周易集解纂疏》的爻位「當」、「應」觀析論 , in 政大中文學報. 2009, 陳睿宏. p. 121-158. [13] 陳威, 《 周易》 卦爻辭同文現象研究, in 臺灣師範大學國文學系學位論文. 2007. p. 1-128. [14] Liu, C.-L., et al. Textual Analysis for Studying Chinese Historical Documents and Literary Novels. in Proceedings of the ASE BigData & SocialInformatics 2015. 2015. ACM. [15] 徐志銳, 周易新譯. 1996: 里仁書局. [16] 傅佩荣, 樂天知命: 傅佩榮談《 易經》. 2011: 天下遠見出版股份有限公司. [17] 周文王, 周易新解. 2015: 華志文化事業有限公司. [18] Feldman, R. and J. Sanger, The text mining handbook: advanced approaches in analyzing unstructured data. 2007: Cambridge University Press. [19] Roberts, C.W., A conceptual framework for quantitative text analysis. Quality and Quantity, 2000. 34(3): p. 259-274. [20] Carroll, J.M. and R. Roeloffs, Computer selection of keywords using word-frequency analysis. American Documentation (pre-1986), 1969. 20(3): p. 227. [21] Pak, A. and P. Paroubek. Twitter as a Corpus for Sentiment Analysis and Opinion Mining. in LREC. 2010. [22] Cavnar, W.B. and J.M. Trenkle, N-gram-based text categorization. Ann Arbor MI, 1994. 48113(2): p. 161-175. [23] Jurafsky, D. and J.H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. MIT Press. [24] Salton, G., Automatic text processing: The transformation, analysis, and retrieval of. Reading: Addison-Wesley, 1989. [25] Chowdhury, G., Introduction to modern information retrieval. 2010: Facet publishing. [26] Borgatti, S.P. and P.C. Foster, The network paradigm in organizational research: A review and typology. Journal of management, 2003. 29(6): p. 991-1013. [27] Freeman, L.C., Centrality in social networks conceptual clarification. Social networks, 1979. 1(3): p. 215-239. [28] Carrington, P.J., J. Scott, and S. Wasserman, Models and methods in social network analysis. Vol. 28. 2005: Cambridge university press. [29] Carley, K.M., Network text analysis: The network position of concepts. Text analysis for the social sciences: Methods for drawing statistical inferences from texts and transcripts, 1997: p. 79-100. [30] Diesner, J. and K.M. Carley, Revealing social structure from texts. Causal mapping for research in information technology, 2004: p. 81. [31] Martin, M.K., J. Pfeffer, and K.M. Carley, Network text analysis of conceptual overlap in interviews, newspaper articles and keywords. Social Network Analysis and Mining, 2013. 3(4): p. 1165-1177. [32] Hunter, S.D. and S. Smith, Center of Attention: A Network Text Analysis of American Sniper. 2015. [33] Hunter, S. and S. Singh, A Network Text Analysis of Fight Club. Theory and Practice in Language Studies, 2015. 5(4): p. 737-749. [34] Schütze, H. and J.O. Pedersen, A cooccurrence-based thesaurus and two applications to information retrieval. Information Processing & Management, 1997. 33(3): p. 307-318. [35] Sudhahar, S., G.A. Veltri, and N. Cristianini, Automated analysis of the US presidential elections using Big Data and network analysis. Big Data & Society, 2015. 2(1): p. 2053951715572916. [36] Özgür, A., B. Cetin, and H. Bingol, Co-occurrence network of reuters news. International Journal of Modern Physics C, 2008. 19(05): p. 689-702. [37] Liang, W., et al., Co-occurrence network analysis of modern Chinese poems. Physica A: Statistical Mechanics and its Applications, 2015. 420: p. 284-293. [38] Leydesdorff, L. and P. Zhou, Co‐word analysis using the Chinese character set. Journal of the American Society for information Science and Technology, 2008. 59(9): p. 1528-1530. [39] Cohen, A.M., et al., Using co-occurrence network structure to extract synonymous gene and protein names from MEDLINE abstracts. BMC bioinformatics, 2005. 6(1): p. 103. [40] Feicheng, M. and L. Yating, Utilising social network analysis to study the characteristics and functions of the co-occurrence network of online tags. Online Information Review, 2014. 38(2): p. 232-247. [41] Borgatti, S.P. and M.G. Everett, Models of core/periphery structures. Social networks, 2000. 21(4): p. 375-395. [42] 李镜池, 周易筮辞续考. 周易探源, 1978. |
Description: | 碩士 國立政治大學 資訊科學學系 101753035 |
Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0101753035 |
Data Type: | thesis |
Appears in Collections: | [資訊科學系] 學位論文
|
Files in This Item:
File |
Size | Format | |
303501.pdf | 2933Kb | Adobe PDF2 | 285 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|