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.

隨著社群媒體的普及，新聞媒體與意見領袖逐漸重視在社群媒體上以貼文方式發佈新聞資訊，社群媒體成為許多使用者會接收新聞與重大事件的主要管道且透過社群媒體的評論、分享與按讚等互動機制表達立場，這些即時互動行為是傳統媒體缺乏的機制，如何分析也是研究上的挑戰。本研究將針對Facebook上的貼文與互動行為進行分析，提供一款互動視覺化系統，找出貼文資料集中相似的貼文群集以及隨著時間推移下貼文屬性的變化，進一步瞭解Facebook上使用者、貼文與重大事件之間的相互影響。由於Facebook上的貼文與互動行為具多維度屬性，我們透過降維演算法將大量的貼文以二維散佈圖呈現，達到將相似貼文分群的效果。另外，我們設計了一種視覺化呈現方法，「Time Block」，突顯出時間的推移下貼文屬性的變化，藉此觀察出貼文資料集是否存在特定的模式。最後提供即時互動的操作介面，以及貼文屬性以及關鍵字兩者的統計，藉此連接到貼文集的屬性與時間的分佈關係，協助以視覺化方式進行探索與分析。最後，透過案例分析與使用者測試呈現此視覺化探索工具的優缺點。 / Social media becomes an essential medium for broadcast news. News media and option leader post information and people love to receive news and interactive using comment and likes to feedback. It is a research challenge to analysis this massive amount interactive behavior data in social media. In this paper, we propose an interactive visualization system to explore on the posts and interactions on Facebook. This system can help a user to find out the similar interactive behavior cluster and the trend of time-varying attributes to understand how the users, posts, and a big event to affect each other. Facebook Posts and interactive behavior contains multiple dimensional attributes; we adopt the dimensional reduce algorithm and 2D scatter plot to present the cluster in the spatial domain. Then, we design a time-varying visualization method, `Time Block,' can highlight the changing attributes and observe the unique pattern in the time domain. Also, we design a real-time interactive interface to connect the cluster and trend visualization with additional keyword distribution and attribute statistics. Finally, we use case study and user study to demonstrate the advantage of the proposed system.

隨著互動式資料視覺化工具越來越多，設計者需要一個方法來衡量其作品是否好用、能否被理解、使用效率高低。互動式資料視覺化需要透過使用者的互動才能觀察到資料的不同面向，再進一步產生洞見，然而現有的評估方式多僅聚焦於靜態資料圖表，設計者無法從中得知使用者的操作困難之處，並據此進行加強與改善，因此本研究提出一個整合量化分析與質化記錄的系統性評估方式，應用於互動式資料視覺化的優使性(usability)分析。 本研究的方法為追蹤使用者的眼動和滑鼠操作過程，先將其記錄成量化數據，透過興趣區域的標定與將轉換使用者行為成序列後，進行序列運算和統計分析；同時，從使用者經驗研究方法得到實驗過程的質化記錄，用來輔助解釋量化分析的結果。 本論文藉由兩個互動式資料視覺化工具來驗證以眼動與滑鼠追蹤評估互動式資料視覺化是可行的，我們提出了具體的實驗流程、量化紀錄與分析方式，並建議以下評估指標：吸引力、易發現性、困難度、易識別性、易理解性、精準表達程度、細部困難度、使用效率。 / As more and more interactive data visualization tools emerge, designers need an organized evaluation method to provide timely feedback and understand user behavior. In contrast to traditional graphical presentations, interactive data visualization tools call for user manipulation to gain specific insights. It is therefore imperative to study the intermediate operation process, rather than the final outcome, to provide a critical understanding of the developed tool. Toward this objective, we propose a systematic approach combining quantitative analysis and qualitative assessment to gauge the usability of interactive data visualization tools in this research. Firstly, quantitative data including gaze and mouse movements are collected. By combining the definition of area of interest, these trajectories can be converted into user sequences, which are conveniently accessible for further statistical analysis as well as path comparison. Secondly, qualitative information obtained by observing user operation is gathered to offer additional insight and complement/support conclusions obtained from quantitative analysis. Two interactive data visualization tools are employed to examine the feasibility and universality of our experimental and analytical procedure. To conclude, we come up with several key indicators to evaluate interactive data visualization, including attraction, discoverability, difficulty, identifiability, comprehensibility, precision of expression, difficulty(detailed) and efficiency.

本研究延續Coffey (2013)的研究，將社會學的理論「自主概念」應用至華語教學研究的領域當中，採用的是敘事資料、個案研究方法。本研究中，有兩位敘事風格與學習行動迥異的華語老師，一位平穩的將自己的故事娓娓道來；另一位則關注觀眾的感受，使得說故事的過程趣味橫生。本研究以兩位老師回顧二語學習過程的音檔及書面資料為分析樣本，並依照他們敘事的時間繪製曲線圖，這兩張曲線圖包含了敘事場域中的三個不同面向，包括：兩位老師學習過程中的各個事件和人生轉捩點（二語學習自主）、說話者是否在乎觀眾反應而改變原先的說話方式或內容（說話者展現之言談自主）以及觀眾的反應。研究人員依話語給予相應不同分數，並利用Holistic-Form（整體形式）的方式繪出三面向之間的交會與變化，從中分析學習者如何敘說自己生命歷程以及自我概念的變化。 本研究之貢獻在於敘事研究的分析方法上，使用三條曲線視覺化學習者自我與時空連結與敘事現場氣氛的流動，更全面的檢視團體互動細節。並發現這種「依附/不依附」觀眾為中心的敘事模式，顯示說故事者的自我揭露程度不同。以此提出建議，當說話者展現之言談自主高、情節多且短時，要注意對方可能避重就輕、迴避了感受的真實性。同時，言談自主高起的區段可能是當事人比較痛苦或負面的經驗，可以多加留意。研究的場域與在場的觀眾會對說故事者的敘事產生影響，如採一對一訪談，應能降低前述情況的發生。並建議將之納入日後研究方法的範疇中。 / Drawing on theories of identity and agency (particularly, Coffey, 2013), this qualitative case study scrutinizes how language learner identities and agencies are performed in group storytelling sections. The participants involved are two Chinese teachers with distinct narrative styles: one tells her stories quite uneventfully; while the other intentionally shaped her stories according to audience response, making the storytelling section full of laughter. Both oral and written narratives were gathered for holistic-form analysis which resulted in two matrix displays of running time, response intensity, and levels of learner agency as the participants narrated critical events in their language learning trajectories. These matrixes helped reveal how the three intersected and changed and how the participants narrated the changing identities and agency in the L2 stories that they lived through. This study contributes to the approach of data analysis in narrative study by utilizing graphical presentations to facilitate visualization and analysis of the interaction between and among the storytelling time/context, the participants’ language learner agency, and audience response. It also pays close attention to details in how the learner story is told and in what kind of group dynamics as well as reveals different possible levels of self-exposure: that is, audience-centered and non-audience-centered narrative styles. In addition, the study alerts narrative researchers of the possibility that true emotions will be hidden and important details will be avoided when the narrator performs high agency in discourse, particularly with too many short plots in their stories. It is also clear that when there is high intensity in changing discourse it is often involved emotionally charged negative episodes that deserve careful scrutiny. Since context and audience could affect the form, flow, and content of narratives, one-on-one interview is suggested for future study to avoid limitations introduced by group storytelling sections.