| 研究生: |
陳湘怡 Chen, Shiang-Yi |
|---|---|
| 論文名稱: |
以物理碰撞為基礎的動態文字雲視覺化技術 Temporal Visualization using Physics based Dynamic Tag clouds |
| 指導教授: |
李同益
Lee, Tong-Yee |
| 學位類別: |
碩士 Master |
| 系所名稱: |
電機資訊學院 - 資訊工程學系 Department of Computer Science and Information Engineering |
| 論文出版年: | 2014 |
| 畢業學年度: | 102 |
| 語文別: | 英文 |
| 論文頁數: | 34 |
| 中文關鍵詞: | 文字雲 、隨時間變化的文本數據 、資訊的視覺化 |
| 外文關鍵詞: | word cloud, time-varying text data, information visualization |
| 相關次數: | 點閱:177 下載:7 |
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Word Cloud 是目前許多網站用來表示關鍵字的視覺化工具,主要依照重要程度來改變字體大小或顏色,特性包含具有歸納重點的能力、搜尋資訊時間縮短及顏色主題性。大部分相關研究著重於排列最佳化及處理具時間序列型態的可視化,但是缺乏考慮時間變化及相關性以及不同 Word Cloud 的形狀變化,依時間性動態變化是最直接影響人類視覺系統的重要線索,本論文提出一種新的具有處理時間性資料特性的可視化動態文字雲,同時具有故事敘述特性的視覺化工具。藉由物理碰撞引擎將具有時間性的字詞安排在不同特定的形狀序列裡。每個字詞視為一個剛體,搭配設計好的準則包含幾何美學與時間維持的限制條件產生具有時間性且同時可變形的Word Cloud,不僅安排字詞在不同形狀下的擺放位置同時也隨著時間遞增均勻穩定地轉換不同形狀,從而產生滿足人類視覺感受的可視化結果。
A word cloud is a visual representation of a collection of text documents that uses various font sizes and colors to depict significant words. The majority of previous studies on time-varying word clouds focuses on layout optimization and temporal trend visualization. However, they do not fully consider the temporal motions, temporal coherence, and spatial shapes of word clouds,which are important cues for human visual systems in capturing information from time-varying text data. This paper presents a novel method that uses rigid body dynamics to arrange multi-temporal word tags in a specific shape sequence under various constraints. Each word tag is regarded as a rigid body in dynamics. With the aid of geometric, aesthetic, and temporal coherence constraints, the proposed method can generate a temporally morphable word cloud that not only arranges word tags in their corresponding shapesbut also smoothly transforms the shapes of word clouds over time, thus yielding a pleasing time-varying visualization.
The generated word-cloud morphing can be an effective storytelling and visualization tool that illustrates the temporal changes in both the word tags and the shapes of the word clouds. Experiment results on various data demonstrate the feasibility and flexibility of the proposed method in morphable word cloud generation.
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