Web 2.0的提出，主要的概念是以Web為平台，以「個人」為中心，透過群體智慧的方式來共享與產生知識，例如維基百科、部落格等。部落格提供了個人自由創作與發表文章空間，主要以RSS、Trackback為共有標準，服務提供者可另外加上自訂功能。然而部落格每天所產生的文章量相當龐大，我們是否有辦法在這些文章中，找出符合使用者想看的文章。本研究期望建構一個部落格入口網站，分析目前部落格使用的特徵，比較與目前Web環境差異；引入語意網技術，針對Metadata處理資訊，設計本體論(Ontology)來描述人、文章與標籤之間的關係並建立簡單分類；導入大眾既有經驗與人脈網路建立，觀察社會網路所能提供的貢獻；實作上將透過特徵分析來設計Crawler，自動抓取並解析文章，並建置入口網站，進行資料的分析與驗證，探討加入語意網與社會網路分析的結合所能帶來的效益。 / The Web 2.0 is based on the main concept "individuals" as the center, through the collaborative wisdom to share and to generate knowledge on the Web, such as the Wikipedia, Blog, etc. Blog provides a space for the free creativity and posting articles from individuals. Based on RSS and Trackback service providers can set an additional function. However, the daily amount of articles issued from the Blog is enormous. How can we provide methods for users to find their interesting articles? This study hopes to build the Blog portal and analysis of the current Blog features compared with the web environment. We use semantic web technology and focus on metadata processing. The ontology describes the relationship among persons, articles, tags and a simple categorization. Folks experience and relationship are established and observed with the benefits from social network analysis. In this study, we implement a crawler, and automatically grab and analysis articles. With constructing the portal, we extract information and discuss the benefits of using combination semantic web and social network analysis

隨著資訊科技的發展，數位學習的觀念逐漸興起，在二十一世紀強調知識經濟的今天，自主學習及問題解決能力的養成更顯重要，而藉由網路進行問題導向合作學習，學習者可更方便的透過自主學習方式培養問題解決能力。然而學習者在進行網路合作學習的互動期間會接收到大量來自同儕的資訊，有些學習者常會因為無法判斷資訊的正確性，而無法有效選擇、判斷、分析與整合所獲得的資訊，進而觀望同儕或是意見領袖的意見。因此，本研究利用學習者在問題導向網路合作學習歷程中所產生的社會網路互動資料，利用品質Q函數結合基因算法進行社群探勘，並搭配PageRank演算法找尋出每個社群中的較意見領袖，探討採用教師直接進行資訊傳播的一級資訊傳播模式與透過社群意見領袖進行資訊傳播的二級資訊傳播模式對於學習者的學習成效、社會網路互動及團體凝聚力的影響。此外，也探討採用這兩種資訊傳播模式的不同性別及不同人格特質學習者的學習成效、社會網路互動及團體凝聚力是否具有顯著差異。 研究結果發現：(1)在問題導向網路合作學習環境下，採用發佈訊息給意見領袖之二級資訊傳播模式的實驗組學習者，在學習成效上顯著優於教師透過網站公告之一級傳播模式的控制組學習者；(2)在問題導向網路合作學習環境下，採用發佈訊息給意見領袖之二級資訊傳播模式的實驗組女性學習者，在學習成效上顯著優於透過教師網站公告之一級資訊傳播模式的控制組女性學習者，但兩組男性學習者之間則無顯著差異；(3)在問題導向網路合作學習環境下，採用發佈訊息給意見領袖之二級資訊傳播模式的實驗組學習者，在促進同儕互動成效上顯著優於教師透過網站公告之一級傳播模式的控制組學習者;(4)透過品質Q函數結合基因演算法偵測社群，以及使用PageRank找尋社群意見領袖之方法，能精確的協助教師找到問題導向網路合作學習社群之意見領袖。 最後，根據研究結果，本研究提出教學實施及未來研究方向建議，供後續研究參考以進行更深入的探究。 / The concept of e-learning gradually emerges with the development of information technology. In the 21st century when knowledge economy is emphasized, the cultivation of self-directed learning and problem-solving ability becomes more important. Learners with problem-based cooperative learning through networks can more conveniently cultivate the problem-solving ability with self-directed learning. Nonetheless, learners would receive large amount of peer information during the network cooperative learning interaction; some learners therefore could not effectively select, judge, analyze, and integrated the acquired information by judging the accuracy of information to further observe the opinions of peers or opinion leaders. For this reason, learners’ social network interaction data generated in the problem-based network cooperative learning process are proceeded community mining by combining quality function Q and genetic algorithm, and PageRank algorithm is applied to search for the opinion leader in each community in order to discuss the effects of teachers directly proceeding first-order information communication model and second-order information communication model through community opinion leaders on learners’ learning outcome, social network interaction, and group cohesiveness. Furthermore, the effects of such two information communication models on learning outcome, social network interaction, and group cohesiveness of learners with different genders and personality traits are also investigated. The research findings show (1) learners in the experimental group with second-order information communication model by distributing information to opinion leaders, under the problem-based network cooperative learning environment, significantly outperform learners in the control group with first-order communication model through network announcement on the learning outcome; (2) female learners in the experimental group with second-order information communication model by distributing information to opinion leaders, under the problem-based network cooperative learning environment, present remarkably better learning outcome than female learners in the control group with first-order information communication model through network announcement, while no significant difference appears between male learners in both groups; (3) learners in the experimental group with second-order information communication model by distributing information to opinion leaders, under the problem-based network cooperative learning, notably show better peer interaction effectiveness than learners in the control group with first-order communication model through network announcement; and (4) combining quality function Q with genetic algorithm to detect community and applying PageRank to search for community opinion leaders could accurately assist teachers in finding out the problem-based network cooperative learning community opinion leaders. Finally, suggestions for teaching practice and future research, according to the research results, are proposed in this study for successive research.

全球智慧型手機的出貨量持續提升，且熱門市集的App下載次數紛紛突破500億次。而在iOS和Android手機App市集中，App的評價和評論對App在市集的排序有很大的影響；對於App開發者而言，透過評論確實可掌握使用者的需求，並在產生抱怨前能快速反應避免危機。然而，每日多達上百篇的評論，透過人力逐篇查看，不止耗費時間，更無法整合性的瞭解使用者的需求與問題。 文字情感分析通常會使用監督式或非監督式的方法分析文字評論，其中監督式方法被證實透過簡單的文件量化方法就可達到很高的正確率。但監督式方法有無法預期未知趨勢的限制，且需要進行耗費人力的文章類別標注工作。 本研究透過情感傾向和熱門關注議題兩個面向來分析App評論，提出一個混合非監督式與監督式的中文情感分析方法。我們先透過非監督式方法標注評論類別，並作視覺化整理呈現，最後再用監督式方法建立分類模型，並驗證其效果。 在實驗結果中，利用中文詞彙網路所建立的情感詞集，確實可用來判斷評論的正反情緒，唯判斷負面評論效果不佳需作改善。在議題擷取方面，嘗試使用兩種不同分群方法，其中使用NPMI衡量字詞間關係強度，再配合社群網路分析的Concor方法結果有不錯的成效。最後在使用監督式學習的分類結果中，情感傾向的分類正確率達到87%，關注議題的分類正確率達到96%，皆有不錯表現。 本研究利用中文詞彙網路與社會網路分析，來發展一個非監督式的中文類別判斷方法，並建立一個中文情感分析的範例。另外透過建立全面性的視覺化報告來瞭解使用者的正反回饋意見，並可透過分類模型來掌握新評論的內容，以提供App開發者在市場上之競爭智慧。 / While the number of smartphone shipment is continuesly growing, the number of App downloads from the popular app markets has been already over 50 billion. By Apple App Store and Google Play, ratings and reviews play a more important role in influencing app difusion. While app developers can realize users’ needs by app reviews, more than thousands of reviews produced by user everday become difficult to be read and collated. Sentiment Analysis researchs encompass supervised and unsupervised methods for analyzing review text. The supervised learning is proven as a useful method and can reach high accuracy, but there are limits where future trend can not be recognized and the labels of individual classes must be made manually. We concentrate on two issues, viz Sentiment Orientation and Popular Topic, to propose a Chinese Sentiment Analysis method which combines supervised and unsupervised learning. At First, we use unsupervised learning to label every review articles and produce visualized reports. Secondly, we employee supervised learning to build classification model and verify the result. In the experiment, the Chinese WordNet is used to build sentiment lexicon to determin review’s sentiment orientation, but the result shows it is weak to find out negative review opinions. In the Topic Extraction phase, we apply two clustering methods to extract Popular Topic classes and its result is excellent by using of NPMI Model with Social Network Analysis Method i.e. Concor. In the supervised learning phase, the accuracy of Sentiment Orientation class is 87% and the accuracy of Popular Topic class is 96%. In this research, we conduct an exemplification of the unsupervised method by means of Chinese WorkNet and Social Network Analysis to determin the review classes. Also, we build a comprehensive visualized report to realize users’ feedbacks and utilize classification to explore new comments. Last but not least, with Chinese Sentiment Analysis of this research, and the competitive intelligence in App market can be provided to the App develops.

近幾年在金融風暴及全球競爭等影響下，企業紛紛導入商業智慧平台，提供管理階層可簡易且快速的分析各種可量化管理的關鍵指標。但在後續的推廣上，經常會因商業智慧系統提供的資訊過於豐富，造成使用者在學習階段無法有效的取得所需資訊，導致商業智慧無法發揮預期效果。本論文以使用者在商業智慧平台上的操作相似度進行分析，建立相對於實體部門的凝聚子群，且用中心性計算各節點的關聯加權，整合至所設計的推薦機制，用以提升商業智慧平台成功導入的機率。經模擬實驗的證實，在推薦機制中考慮此因素會較原始的推薦機制擁有更高的精確度。 / In recent years, enterprises are facing financial turmoil, global competition, and shortened business cycle. Under these influences, enterprises usually implement the Business Intelligence platform to help managers get the key indicators of business management quickly and easily. In the promotion stage of such Business Intelligence platforms, users usually give up using the system due to huge amount of information provided by the BI platform. They cannot intuitively obtain the required information in the early stage when they use the system. In this study, we analyze the similarity of users’ operations on the BI platform and try to establish cohesive subgroups in the corresponding organization. In addition, we also integrate the associated weighting factor calculated from the centrality measures into the recommendation mechanism to increase the probability of successful uses of BI platform. From our simulation experiments, we find that the recommendation accuracies are higher when we add the clustering result and the associated weighting factor into the recommendation mechanism.

如何從大量的資料中擷取隱匿或不容易直接觀察的資訊，是重要的議題，社會網路提供了一個適當的系統描述模型與內部檢視分析的方法，過去社會網路分析多著重於靜態的分析，無法解釋發生在網路上的動態行為；我們的研究目的是從動態社會網路分析的角度，觀察政治權力的變化，將資料依時間切分成多個資料集，在各個資料集中，利用官員共同異動職務及共事資料建構網路，並使用EdgeBetweenness分群方法將網路做分群，以找出潛在的政治群組，接著再採用事件導向的方法(Event-based Framework)，比較連續兩個時間區間的網路分群結果，以觀察政治群體的動態發展，找出政治群組事件，並將其匯集成政治群組指標，以用來衡量政治群組的變動性及穩定性。我們提供了一個觀察政治權力變化的模型，透過網路建立、網路分群到觀察網路動態行為，找到不容易直接取得的資訊，我們也以此觀察模型解決以下問題：(1)觀察部門之接班梯隊之變化，(2)觀察特定核心人物之核心成員組成模式，(3)部門專業才能單一性或多元性之觀察。實驗結果顯示，利用政治群組事件設計的政治群組指標，可實際反應政府部門選用人才的傾向為內部調任或外部選用。 / Extracting implicit information from a considerable amount of data is an important intelligent data processing task. Social network analysis is appropriate for this purpose due to its emphasis on the relationship between nodes and the structure of networked interactions. Most research in the past has focused on a static point of view. It can't account for whatever action is taking place in the network. Our research objective is to observe the evolution of political power by dynamic social network analysis. We begin by creating static graphs at different time according to the synchronous job change between the government officials or the relationship between the government officials whom work in the same government agency. We obtain political communities from each of these snapshot graphs using edge betweenness clustering method. Next we define a set of evolutionary events of political communities using event-based framework. We compare two consecutive snapshots to capture the evolutionary events of political communities. We also develop two evolutionary political community metrics to measure the stability of political communities. We propose a model of observing the evolution of political power by three steps－network construction, community identification and community evolution tracking. The approach is shown to be effectual for the purposes of: (1) finding succession pool members in government agencies, (2) observing the inner circle of a leading political figure, (3) measuring the specialized degree of government agencies. Experiments also show that our community evolution metrics reflect the tendency of whether a government agency conducts internal succession or outside appointment.

近年來以物聯網為概念的相關應用已經成為最熱門的議題之一，各國家與企業皆致力於發展其技術以及相關應用。而物聯網的應用範圍十分廣泛，舉凡交通、醫療、電力、物流、家居等都是其應用的範圍。亞洲國家於近十年來也紛紛提出相關發展計畫，由此可看出各國對發展物聯網產品之野心與競爭。而台灣政府於2008年開始積極推展智慧家庭政策，端看目前成果，以技術層面而言並非無法達成，但在實際應用與推廣上明顯仍有許多不足之處。因此本研究欲探討使用者對於物聯網相關應用產品的使用動機為何，以智慧家庭為例，先進行相關文獻探討，並以修正式德菲法做為發展ANP專家問卷的基礎，再利用ANP法施行專家問卷，排序出影響消費者使用動機的各項權重，選擇出最符合消費者需求的產品組合，讓開發者能了解在開發與推展智慧家庭相關應用時，應滿足消費者哪些心理層面之動機因素，提升未來發展相關產品時的成功機率。 / In recent years, applications related to the concept of IoT become one of the most popular issues all over the world. Countries and enterprises devote themselves to developing the technique and application related to IoT. The range of its applications is very wide, including transportation, medical treatment, electricity, logistics, and home, etc. Asian countries set forth some development projects in the past decade, demonstrating the ambition and competition between countries in developing IoT products. Taiwan has started to push the development of smart home related applications since 2008. After development for some years, insufficiency in actual implementation and diffusion remains given our technological advantage. Therefore, this research intends to study the motivation for users to use IoT products taking smart home as an example. First, we study the related literature review and use modified Delphi method to develop the ANP expert questionnaires. Then, we prioritize the weights of consumer’s motives. The result can enable developers of smart home products to understand what kind of consumer motives they should satisfy when developing and promoting smart home applications. This enhances the probability of success for developing related products in the future.