基於標記式主題模型之資料視覺化研究與實現

Abstract

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\n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。\n\n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\n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.\n\nThere 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.