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系統識別號 U0026-0308202019575800
論文名稱(中文) 用於災難事件管理之線上訊息分群技術
論文名稱(英文) Clustering Online Event-based Messages for Disaster Management
校院名稱 成功大學
系所名稱(中) 工程科學系
系所名稱(英) Department of Engineering Science
學年度 108
學期 2
出版年 109
研究生(中文) 楊智傑
研究生(英文) Zhi-Jie Yang
學號 N96071156
學位類別 碩士
語文別 中文
論文頁數 38頁
口試委員 指導教授-侯廷偉
指導教授-鄧維光
口試委員-王明習
口試委員-曾紹崟
中文關鍵字 災難管理  災難偵測  訊息分群  社群媒體 
英文關鍵字 disaster management  disaster detection  messages clustering  social media 
學科別分類
中文摘要 自然災害和人為災害,例如大型交通事故、大型火災,甚至是2019年年底所爆發的新冠肺炎都導致了嚴重的人員傷亡以及經濟損失,因此對於相關災難管理單位來說,掌握最新且準確的災難事件情況是最主要的任務之一。社群媒體也是災難訊息的來源之一,本研究搜尋社群媒體訊息,並採用資料分類技術以期能更有效的找出災難訊息。本研究採用了LibShortText的SVM技術針對短文章進行分類。首先,希望利用到目前為止所蒐集到的災難社群媒體訊息來改善SVM分類器的準確率,因此本研究設計了提升SVM分類器模型對災難訊息分類準確率的實驗。實際成果顯示,改善後的SVM分類器模型平均準確率較原先模型提升24.4%。其次,本研究提出對歷史災難訊息的分群方法,將災難訊息透過Doc2vec轉換為向量,再經由分群演算法將相似的文章向量進行合併,最後透過後處理產生最終結果。實驗結果顯示,在災難發生後,本研究提出的災難訊息分群方法能夠幫助災難管理單位全面地回顧歷史災難事件。
英文摘要 Natural and man-made disasters, such as traffic accidents, fires and even the COVID-19 have caused serious casualties and economic losses, so disaster management is increasingly important. The disaster relief organizations require to have the latest and accurate situation of disaster events. With the widespread social media in recent years, social platforms such as Twitter, Facebook, PTT, etc. allow users to quickly and conveniently share real-time information, like relevant information or photos, videos, etc. to friends, relatives, or communities. Hence social media becomes a source of disaster information. In this research, a scheme combined with disaster message detection and disaster message clustering is proposed. It is demonstrated on a web-based system on which the disaster information is displayed. Firstly, experiments were performed to improve the original SVM classifier model of disaster information detection. Following the experimental results, the model was tuned. The precision of disaster messages detection increased 24.4%. Secondly, a clustering method for historical disaster information is proposed. The short texts are transformed into vectors by Doc2vec. Similar document vectors are clustered by the clustering algorithms. Final clustering result (disaster events) is determined in the post process. Experimental results show that the clustered disaster messages can help the disaster management organization to review historical disaster events after the disaster.
論文目次 第一章 簡介 1
1.1 動機與概述 1
1.2 研究背景與貢獻 2
第二章 相關研究 3
2.1 災難管理 3
2.1.1 WebEOC 5
2.1.2 澳洲危機協調中心 (Crisis Coordination Centre) 6
2.2 社群媒體災難訊息偵測 7
2.2.1. 災難訊息偵測方法 7
2.2.2. SVM分類器的成效 8
2.3 社群媒體訊息以及短文分群相關研究 9
2.3.1 詞頻(Term Frequency) 9
2.3.2 分群演算法 10
第三章 研究方法 11
3.1 災難訊息來源以及研究方法流程 11
3.2 資料前處理 12
3.2.1 斷詞以及移除停用詞 13
3.2.2 過濾雜訊並建構關鍵字文章 13
3.2.3 Doc2vec[15] 14
3.3 分群演算法 16
3.3.1 文章分群演算法種類及比較 16
3.3.2 演算法選擇與閥值設定 18
3.4 資料後處理 19
第四章 實驗探討 20
4.1 實驗規劃 20
4.2 SVM訓練參數調整實驗 21
4.2.1 SVM分類器新模型準確率結果 22
4.3 災難訊息偵測與災難事件統整系統 24
4.4 災難訊息分群實驗 27
4.4.1 案例探討-新冠肺炎趨勢 28
4.4.2 案例探討-錢櫃失火事件 29
4.4.3 案例探討-無重大災難事件 30
4.5 實驗結果探討 31
第五章 結論與未來目標 33
5.1 結論 33
5.2 未來目標 34
參考文獻 35

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