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    题名: 基於標記式主題模型之資料視覺化研究與實現
    A study of data visualization based on labeled topic model and its implementation
    作者: 曾子芸
    贡献者: 陶亞倫
    蔡銘峰
    曾子芸
    关键词: 資料視覺化
    文字資料視覺化
    主題模型
    日期: 2017
    上传时间: 2017-04-05 15:42:07 (UTC+8)
    摘要: 隨著文字資訊的爆炸式增長，越來越多的訊息開始以電子文本的形式儲存及傳遞。但隨著文本內容資訊量不斷地增加，使用者也越來越難以快速地掌握文本全貌。因此本研究試圖透過主題模型（TopicModels）、標記式主題模型（Labeled Topic Models）演算法－在自然語言處理領域裡文本探勘的方法，識別出大規模文本中潛藏的主題訊
    息之後，再利用圖像視覺化在資訊表達上的優勢和效率，透過各種視覺化圖案的呈現從不同的角度來探索文本，形成一種嶄新的大規模文本閱讀與分析方式。

    本研究設計了兩階段實驗：第一階段任務導向性實驗、第二階段指定任務實驗，以及評估問卷來驗證本介面的易用性（ Ease-of-use ）和有用性（ Usefulness ）。並透過實驗問卷的分數結果驗證了，本研究所設計之介面在實務上的確能輔助專家學者進行文本相關研究，也能
    讓對文本熟悉程度不一的使用者在利用此介面探索文本的過程中，更快速地掌握大規模文本的事件全貌。
    With the explosion of text information, there are more and more data being recorded and transmitted in the form of texts. However, as the amount of textual information becomes larger, how to effectively and efficiently realize the information also becomes more difficult. This study attempts to use the Topics Models, text-mining techniques to identify the important topics in the large textual information. In addition, this study also aims to use the techniques of data visualization to present the most informative and valuable details within the large texts.

    There are two parts in this work: the first part is the introduction of text mining algorithms and the second part is the design of the data visualization.Moreover, in the experiments, we also conduct several surveys to verify the proficiency and usefulness and the visualization design. The results of the experiments and surveys, supports that our design provides an effective and efficient interface for users to understand a large set of texts, even for the experts familiar with the corpus.
    參考文獻: [1] W. Albert and T. Tullis. Measuring the user experience: collecting, analyzing, and presenting usability metrics. Newnes, 2013.
    [2] A. Bangor, P. Kortum, and J. Miller. Determining what individual sus scores mean: Adding an adjective rating scale. Journal of usability studies, 4(3):114–123, 2009.
    [3] D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. the Journal of machine Learning research, 3:993–1022, 2003.
    [4] M. Bostock. D3. js. Data Driven Documents, 492, 2012.
    [5] A. J.-B. Chaney and D. M. Blei. Visualizing topic models. 2012.
    [6] J. Chuang, C. D. Manning, and J. Heer. Termite: Visualization techniques for assessing textual topic models. In Proceedings of the International Working Conference on Advanced Visual Interfaces, pages 74–77. ACM, 2012.
    [7] D. Cochran. Twitter Bootstrap Web Development How-To. Packt Publishing Ltd, 2012.
    [8] S. T. Dumais. Latent semantic analysis. Annual review of information science and technology, 38(1):188–230, 2004.
    [9] S. Eppinger and K. Ulrich. Product design and development. McGraw-Hill Higher Education, 2015.
    [10] K. Finstad. The system usability scale and non-native english speakers. Journal of usability studies, 1(4):185–188, 2006.
    [11] T. Hofmann. Probabilistic latent semantic indexing. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pages 50–57. ACM, 1999.
    [12] P. Legris, J. Ingham, and P. Collerette. Why do people use information technology? a critical review of the technology acceptance model. Information & management, 40(3):191–204, 2003.
    [13] R. Likert. A technique for the measurement of attitudes. Archives of psychology, 1932.
    [14] J. Murdock and C. Allen. Visualization techniques for topic model checking. In AAAI, pages 4284–4285, 2015.
    [15] J. Nielsen and T. K. Landauer. A mathematical model of the finding of usability problems. In Proceedings of the INTERACT’93 and CHI’93 conference on Human factors in computing systems, pages 206–213. ACM, 1993.
    [16] D. Ramage, D. Hall, R. Nallapati, and C. D. Manning. Labeled lda: A supervised topic model for credit attribution in multi-labeled corpora. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1, pages 248–256. Association for Computational Linguistics, 2009.
    [17] M. Rosen-Zvi, T. Griffiths, M. Steyvers, and P. Smyth. The author-topic model for authors and documents. In Proceedings of the 20th conference on Uncertainty in artificial intelligence, pages 487–494. AUAI Press, 2004.
    [18] I. J. Schoenberg. Cardinal spline interpolation. SIAM, 1973.
    [19] B. H. Sheppard, J. Hartwick, and P. R. Warshaw. The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of consumer research, 15(3):325–343, 1988.
    [20] C. Sievert and K. E. Shirley. Ldavis: A method for visualizing and interpreting topics. 2014.
    [21] Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei. Sharing clusters among related groups: Hierarchical dirichlet processes. In NIPS, pages 1385–1392, 2004.
    [22] Y.W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei. Hierarchical dirichlet processes. Journal of the american statistical association, 2012.
    [23] S. Wold, K. Esbensen, and P. Geladi. Principal component analysis. Chemometrics and intelligent laboratory systems, 2(1-3):37–52, 1987.
    [24] N. Yau. Data points: Visualization that means something. JohnWiley & Sons, 2013.
    [25] 賈西平, 彭宏, 鄭啟倫, 石時需, and 江焯林. 基于主題的文檔檢索模型. 華南理工大學學報(自然科學版), 36(9):37–42, 2008.
    描述: 碩士
    國立政治大學
    數位內容碩士學位學程
    103462010
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0103462010
    数据类型: thesis
    显示于类别:[數位內容碩士學位學程] 學位論文

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