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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳銘憲(Ming-Syan Chen) | |
| dc.contributor.author | Cheng-Ying Liu | en |
| dc.contributor.author | 劉承穎 | zh_TW |
| dc.date.accessioned | 2021-06-17T01:17:00Z | - |
| dc.date.available | 2027-08-14 | |
| dc.date.copyright | 2017-08-20 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-14 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67004 | - |
| dc.description.abstract | 社交多媒體網路近年來已成為一個即時且有效的溝通平台。無論
新聞、自然災害或是節慶等消息，人們常藉由社交多媒體網路來分享 並進行資訊的傳播。更由於手持式裝紙的普及與蓬勃發展，人們可以 更即時的藉由社交多媒體平台發布即時訊息，也因此社交多媒體上的 資訊更能直接反映時事。然而，由於巨量的資訊被發布於社交多媒體 平台，對使用者而言，要將所有的訊息街進行瀏覽是相當耗時且費力 的。因此，如何有效地探勘這些巨量的資料或提供這些一目了然的資 訊摘要，便成為一極具潛力的研究方向(例如焦點新聞探索、交通狀 況監測以及自然災害監控)。另一方面，雖然名人、公司行號與各種 組織常透過社交多媒體網路來與其粉絲或客戶進行互動，但由於資 料量過於龐大且會隨時間增加。為了要能夠有效分析這些訊息，藉 由各種不同的，在本論文中，我們首先提出一個超連結探勘方法(簡 稱SURLMINE)來協助找出多媒體網路中值得注意的超連結。有別於以 往的尋找方式，超連結由於沒有語言上的限制，可以使得訊息來源更 加完整與全面。此外，根據我們實驗中所發現的結果，目前Twitter上 以英文發表之資料量淤占總資料量的35%，意即透過超連結的方式來 提供訊息，不但可以提高訊息的完整度，亦可以減少因為不同語言翻 譯所帶來的誤差，因而更有效率的達到即時推薦訊息的效果。此外， 為了能夠有效的掌握訊息串流的概要，我們另外提出一增益式摘要彙 總法，讓使用者能夠一目了然知道整個訊息串流的概要。透過真實資 料實驗，超連結探勘能以高達92%的準確度找出重要的超連結，而增 益式摘要叢集法也能有效找出重要的叢集，並且能夠將離群資料有效 排除。 | zh_TW |
| dc.description.abstract | Social media platforms have emerged as a powerful and real-time means of communication recently. People are using social media to share and
exchange information about any events, ranging from breaking news stories to natural disasters and information about local festivals. With the help of rapid development of mobile technologies, messages posted in social media can typically reflect these events as they happen. However, since the dramatic growth of the social media data, it becomes infeasible for users to read all posts or comments. Therefore, mining and summarizing rich user generated content in social media can present great opportunities for developing many potential applications (e.g., breaking news discovery, traffic monitoring, and natural disaster monitoring.) On the other hand, the celebrities, corporations, and organizations also set up social pages to interact with their fans and the public. Although it is important for them to understand how their fans and customers reacting to certain topics and content, the volume and the rapidly increment nature of social media make it time-consuming to get the overview of a comment stream. Therefore, in this dissertation, we first propose a significant URL mining approach (named SURLMINE) to rank the URL on social media based on various features. Note that URL is a global language without language dependency. It is also worthy to know that only 35\% of tweets on Twitter are posted in English. In other words, mining social media content through URL is able to involve more data from different languages. Most of all, it is efficient and there is no lost in translation. On the other hand, to summarize the comment stream, we propose a real-time incremental short text summarization on comment streams (abbreviated as IncreSTS) to provide an at-a-glance presentation that users can easily and rapidly understand the main points of similar comments. Our experiments conducted on real datasets show that the SURLMINE can reach up to 92\% of precision based on YouTube datasets and the increSTS possesses the advantages of high efficiency, high scalability, and better handling outliers on the target problem. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T01:17:00Z (GMT). No. of bitstreams: 1 ntu-106-D01921027-1.pdf: 1852187 bytes, checksum: 84419bfa0463e0a17b182a92019b5748 (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
致謝 iii 中文摘要 v Abstract vii 1 Introduction 1 1.1 Motivation and Overview of the Dissertation 1 1.1.1 SURLMINE 3 1.1.2 IncreSTS 3 1.1.3 Organization of the Dissertation 4 2 SURLMINE: Significant URLs Mining for Large-Scale and Real-time Social Streams 5 2.1 Introduction 5 2.2 Related Work 7 2.3 Data Crawling and Pre-processing 9 2.3.1 Real-time Crawling 10 2.3.2 URL Statistics 10 2.4 Significant URLs Mining 12 2.4.1 Characteristic Features of Social Messages 13 2.4.2 Follower-Friend Ratio 13 2.4.3 Language Distribution 13 2.4.4 Duration and Period 14 2.4.5 Decay Model 15 2.4.6 SURLMINE Algorithm 15 2.5 Experiments 17 2.5.1 Experimental Design 17 2.5.2 Precision and Efficiency Issues 18 2.6 Summary 20 3 IncreSTS: Towards Real-Time Incremental Short Text Summarization on Comment Streams from Social Network Services 21 3.1 Introduction 21 3.2 Preliminaries 25 3.2.1 Problem Description and System Model26 3.2.2 Related Works 27 3.2.3 Distinguishing Features of IncreSTS 32 3.3 Term Vector Model Representation of Comments 34 3.4 Definitions of Clustering Details 35 3.5 Incremental Short Text Summarization 38 3.5.1 BatchSTS Algorithm: Batch Version 38 3.5.2 IncreSTS Algorithm: Incremental Version 40 3.5.3 Visualization Interface 44 3.6 Performance Evaluation 46 3.6.1 Experimental Design 46 3.6.2 Efficiency Issues 48 3.6.3 Effectiveness Issue49 3.6.4 Effects of Parameters 54 3.6.5 Enhancement of Data Structure Design 55 3.6.6 Case Study and Discussion 56 3.7 Summary 58 4 Conclusion 59 4.1 Conclusion and Future Work 59 Bibliography 61 | |
| dc.language.iso | en | |
| dc.subject | 超連結 | zh_TW |
| dc.subject | 巨量社群串流處理 | zh_TW |
| dc.subject | 增益式彙總 | zh_TW |
| dc.subject | 社群網路服務 | zh_TW |
| dc.subject | incremental scheme | en |
| dc.subject | real-time processing | en |
| dc.subject | large-scale social streams | en |
| dc.subject | social network services | en |
| dc.subject | incremental clustering | en |
| dc.subject | URL | en |
| dc.title | 於即時社群媒體之增益式超連結探勘與訊息串流彙總 | zh_TW |
| dc.title | Incremental Significant URL Mining and Comments Summarization in Real-Time Social Media | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 王勝德(Sheng-De Wang),廖弘源(Mark Liao),鄧維光(Wei-Guang Teng),楊得年(De-Nian Yang) | |
| dc.subject.keyword | 超連結,巨量社群串流處理,增益式彙總,社群網路服務, | zh_TW |
| dc.subject.keyword | URL,incremental scheme,large-scale social streams,real-time processing,incremental clustering,social network services, | en |
| dc.relation.page | 69 | |
| dc.identifier.doi | 10.6342/NTU201703228 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-08-14 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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