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姓名 侯貫中(Kuan-Chung Hou)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 資料視覺化在社群媒體平台主題偵測與追蹤的應用
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摘要(中) 隨著社群媒體的興起,使用者願意在平台上以不同的形式表達立場、評論觀點及分享貼文。社群媒體強調其訊息的即時傳播性,導致串流不斷地產生,使用者如何更快速的從這樣大量的資訊中,瞭解目前熱門的主題、使用者關注的事件等,變成一大挑戰及困難。其中,應用在社群媒體中進行主題偵測與追蹤(Topic Detection and Tracking, TDT)變成一大熱門的研究項目。傳統的TDT研究主要針對結構化高的文章,如新聞文章等,本研究以Facebook作為研究平台,針對公開粉絲專頁的短貼文進行主題偵測與追蹤的研究。

本研究的研究目的為讓使用者更快速地掌握主題之下的事件,並透過資料視覺化的呈現,來將設計的架構以故事劃分、源頭故事偵測、群集偵測、追蹤及故事鏈結偵測,五個主題偵測及追蹤系統應具備的能力,做新聞實例的探討並解釋其商業用途。本研究主要將系統流程區分為三個階段。資料蒐集與擷取:透過Facebook Graph API抓取公開粉絲專頁的貼文資訊,並以關鍵字比對的方式將貼文映射到特定主題;資料分析:透過Incremental TF-DF來抓取貼文的核心特徵字詞並且避免字詞維度過高的問題,接著,透過k-medoids文件分群技術及自適應決定分群數目的演算法來達到自動分群辨別出事件;資料呈現:透過群集分析以及資料視覺化的技術來針對分析結果做大規模呈現。
摘要(英) As the rise of social media, people are more willing to declare their position, give comments and share others’ posts on the platform. Social medias emphasize information immediacy, which leads to stream generate constantly. As a result, how users know the hot topics and the events users interest becomes a difficult challenge. In particular,“Topic Detention and Tracking”(TDT) becomes a popular research project applied on social medias. Traditional TDT research mainly focused on high structured articles, e.g., news articles. This research takes Facebook as the research platform and use “Topic Detention and Tracking” to discuss the short-text documents on the public fan page.

The primary purpose of the research is to allow users to realize events of topics through data visualization using five major themes of detections: story segmentation, first story detection, topic tracking, topic detection, and link detection. The application and capability of these detections and tracking system will then be used for discussion of news and explanation of its commercial purposes. This research divides the system procedure to three stages. The first is data collection and catch, which get the posts information on the public fan pages through the Facebook Graph API and map the posts to certain topic through the keyword mapping. The second stage is data analysis, which get the keywords from the posts by Incremental TF-DF and avoid the problem of excessive term dimension. Then, through the document clustering technology, k-medoids, and the auto-decide clustering numbers algorithm to achieve auto-clustering distinguish events. The third stage is data visualization, which through clustering analysis and data visualization technology to visualize the analysis result in a large scale.
關鍵字(中) ★ 主題偵測與追蹤
★ 資料視覺化
★ 中文語言處理
★ Facebook
★ TF-IDF
★ k-medoids
關鍵字(英) ★ Topic Detection and Tracking
★ Data visualization
★ Chinese natural language processing
★ Facebook
★ TF-IDF
★ k-medoids
論文目次 摘要 i
Abstract v
致謝 vi
目錄 vii
圖目錄 ix
表目錄 xi
一、緒論 1
1-1 研究背景 1
1-2 研究動機 2
1-3 研究目的 5
二、相關研究 6
2-1 主題偵測與追蹤 6
2-2 短文件故事的處理 7
2-2-1 文件基底法 7
2-2-2 特徵基底法 8
2-2-3 機率主題模型 9
2-3 OpView社群觀測平台 10
2-3-1 關鍵字風暴圖 11
三、系統架構 12
3-1 系統概念與流程 12
3-2 資料搜集與擷取 13
3-2-1 貼文評分 13
3-2-2 事件處理 14
3-3 資料分析 17
3-3-1 Jieba中文斷詞程式 17
3-3-2 文件特徵萃取 18
3-3-3 字詞的語義相似度 20
3-3-4 文件的相似度 23
3-3-5 k-medoids分群法 25
3-4 資料呈現 27
3-4-1 分群關鍵字標定 27
3-4-2 資料視覺化 28
四 實驗結果與討論 39
4-1 評估方法 39
4-2 資料集 40
4-3 特徵選取字詞門檻數 41
4-4 同義詞過濾參數 41
4-5 主題自動分群參數 42
4-6 實驗1:系統參數配置 42
4-7 實驗2:系統執行效率比較 44
4-8 實驗3:Word2Vec語料庫對系統表現影響 46
五 結論與未來研究方向 48
5-1 結論 48
5-2 研究限制 48
5-3 未來研究方向 49
文獻探討 50
英文文獻 50
中文文獻 53
參考文獻 英文文獻
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指導教授 林熙禎(She-Jen Lin) 審核日期 2017-7-21
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