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題名 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作者 李俊澔
Lee, Chun Hao貢獻者 劉吉軒
Liu, Jyi Shane
李俊澔
Lee, Chun Hao關鍵詞 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日期 2016 上傳時間 22-八月-2016 11:06:55 (UTC+8) 摘要 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researchers to study different historical text.參考文獻 [1] Chen, S.-P., et al., On building a full-text digital library of historical documents, in Asian Digital Libraries.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. 描述 碩士
國立政治大學
資訊科學學系
101753035資料來源 http://thesis.lib.nccu.edu.tw/record/#G0101753035 資料類型 thesis dc.contributor.advisor 劉吉軒 zh_TW dc.contributor.advisor Liu, Jyi Shane en_US dc.contributor.author (作者) 李俊澔 zh_TW dc.contributor.author (作者) Lee, Chun Hao en_US dc.creator (作者) 李俊澔 zh_TW dc.creator (作者) Lee, Chun Hao en_US dc.date (日期) 2016 en_US dc.date.accessioned 22-八月-2016 11:06:55 (UTC+8) - dc.date.available 22-八月-2016 11:06:55 (UTC+8) - dc.date.issued (上傳時間) 22-八月-2016 11:06:55 (UTC+8) - dc.identifier (其他 識別碼) G0101753035 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/100499 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學學系 zh_TW dc.description (描述) 101753035 zh_TW dc.description.abstract (摘要) 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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 第一章 緒論 11.1 研究背景 11.2 研究動機與目的 21.3 研究資料 41.4 論文架構 5第二章 文獻探討 62.1 文字探勘 (Text Mining) 62.2 詞頻分析(word frequency analysis) 62.3 社會網路分析(Social Network Analysis) 72.4 網路文字分析(Network Text Analysis) 72.5 共現網路(Co-occurrence network analysis) 8第三章 研究方法與系統架構 93.1 研究流程架構 93.2 文本資料前處理 93.2.1 《易經》符號系統結構 103.2.2 《易經》爻位貴賤 103.2.3 《易經》爻辭時序性 113.2.4 爻辭與爻位規則條例 113.2.5 爻辭常見之重要名詞 123.3 詞頻分析 143.3.1 N-gram model 143.3.2 TF-IDF（term frequency–inverse document frequency）143.4 共現網路關聯定義 153.4.1 相鄰關係與標點符號分段定義關聯(relation) 153.4.2 全關係與跨標點符號定義關聯(relation) 163.5 社會網路分析 16第四章 實驗數據與結果 204.1 詞頻分析 204.1.1 1-gram詞頻分析 204.1.2 1-gram TF-IDF 254.1.3 2-gram詞頻分析 304.1.4 2-gram TF-IDF 354.2 社會網路分析 394.2.1 連通子圖 394.2.2 社會網路分析 59第五章 結論與未來研究方向 935.1 研究結論 935.2 未來研究方向 94Reference 96 zh_TW dc.format.extent 3003776 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0101753035 en_US dc.subject (關鍵詞) 易經爻辭 zh_TW dc.subject (關鍵詞) 詞頻分析 zh_TW dc.subject (關鍵詞) 社會網路分析 zh_TW dc.subject (關鍵詞) 資料分析 zh_TW dc.subject (關鍵詞) I Ching Line Statements en_US dc.subject (關鍵詞) Word Frequency Analysis en_US dc.subject (關鍵詞) Social Network Analysis en_US dc.subject (關鍵詞) Data Analysis en_US dc.title (題名) 應用社會網路分析於易經爻辭之文字特徵觀察 zh_TW dc.title (題名) Application of Social Network Analysis For Text Characteristic Observation On I-Ching Line Statements en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [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. zh_TW
