跳到主要內容

臺灣博碩士論文加值系統

(3.236.84.188) 您好!臺灣時間:2021/08/04 23:59
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:林芳瑜
研究生(外文):Fang-YuLin
論文名稱:基於協同意見與互動行為分析之微網誌使用者共鳴關係發掘方法
論文名稱(外文):Resonance-Relationship Discovery Approach for Microblog Users Based on Coordinate Opinion and Interactive Behavior Analysis
指導教授:郭耀煌郭耀煌引用關係
指導教授(外文):Yau-Hwang Kuo
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:68
中文關鍵詞:互動行為分析協調意見分析共鳴關係社群媒體分析
外文關鍵詞:Interactive behavior AnalysisCoordinate Opinion AnalysisResonance-relationshipSocial media analysis
相關次數:
  • 被引用被引用:0
  • 點閱點閱:170
  • 評分評分:
  • 下載下載:8
  • 收藏至我的研究室書目清單書目收藏:0
近年來在線上社群媒體隱性關係分析的領域中,許多研究專注於使用者興趣偵測以及相似度評估。在先前的這些研究中,個人檔案資訊經常被當成分析的基礎,進而加以改良或取得其他分析資訊。然而,如此的分析基礎可能發生真實狀況與個人檔案資訊中的主觀資訊不一致的現象。或是個人檔案資訊中部分資訊是過去參與活動的紀錄資訊,例如Facebook中的頁面標籤,也可能產生與當下真實情況不一致的現象。為了避免這種不一致的情況,因此,我們希望透過客觀資訊進行研究分析。本篇研究中,我們提出一個新的共鳴關係概念,共鳴關係是藉由微網誌中使用者的互動行為以及意見來探討客觀資訊的分析,而不是以個人檔案資訊為基礎。我們利用這些會隨著時間改變的互動資訊分析後建立共鳴關係,並且模組化互動資訊。最後,呈現我們的觀察、實驗結果並且討論可能存在的問題與限制。簡言之,此篇研究我們提出一個新的方法架構分析及解決在線上社群關係研究中的潛在問題。
In the field of hidden relationship analysis of community for online social media, there are lots of researches focusing on interest detection and similarity evaluation. Among conventional studies, personal profile information (explicit data) is usually the main foundation to analyze. However, inconsistency between a real facts and subjective information written by users might occur. Thus, in order to avoid this kind of problem, we think leveraging objective data to analyze might be effective. In this paper, we proposed a new concept of resonance-relationship network by considering objective information about interactive behavior and coordinate opinion on microblogs among users in addition to user profiles. We leverage interactive and time-varying data to discover resonance-relationship, and model the distribution of interactions. Finally, we showed our observation and experiment results, and discussed some problems and restrictions. In summary, we proposed a novel model to analyze and solve potential problems for online social relationships.
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Contribution 4
1.4 Organization 4
Chapter 2 Related Work 5
2.1 User Behavior Modeling 5
2.2 Semantic Orientation Analysis 6
2.3 Social Relationship discovering and matching 7
Chapter 3 Theme-based Resonance Model: TBRM 11
3.1 Structure and Problem Identification 11
3.2 Data Collection 14
3.3 Preprocessing 17
3.3.1 Noise Filter 17
3.3.2 Active Cluster Detection 18
3.4 Coordinate Opinion Analysis 20
3.4.1 Orientation of Opinion 20
3.4.2 Referable Degree Computing 22
3.5 Behavior Pattern Analysis 26
3.5.1 Recognition of Behavior Pattern 27
3.5.2 Computing of Participating Degree 31
Chapter 4 Resonance Degree Determination Model: RDDM 33
4.1 Influence Factors 34
4.2 Resonance Degree Calculation 37
Chapter 5 Experiment Results 40
5.1 Experiments for TBRM 40
5.2 Quantitative Measure for RDDM 44
5.2.1 NDCG for Ensuring Reliability 44
5.2.2 Modularity of Resonance-Relationship Network 46
5.3 Comparison and Discussion 48
Chapter 6 Conclusion and Prospect 52
References 53
Appendix A. 57
Appendix B. 58
Appendix C. 66


[Als11]Alsaleh, S., Nayak, R., Xu, Y., and Chen, L., “Improving matching process in social network using implicit and explicit user information, Web Technologies and Applications, pp. 313-320, 2011.
[Als11]Alsaleh, S., Nayak, R., and Xu, Y., “Finding and Matching Communities in Social Networks Using Data Mining, International Conference on Advances in Social Networks Analysis and Mining, pp. 389-393, 2011.
[Ben09]Benevenuto, F., Rodrigues, T., Cha, M., and Almeida V., “Characterizing user behavior in online social networks, Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference, Chicago, USA, pp. 49-62, 2009.
[Cai05]Cai, D., Shao, Z. He, X. Yan, X., and Han, J., “Mining hidden community in heterogeneous social networks, Proccedings on the 3rd international workshop on Link discovery, Chicago, pp. 58-65, 2005.
[Guo09]Guo L. et al., “Analyzing Patterns of User Content Generation in Online Social Networks, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, Pages 369-378, 2009.
[Gya10]Gyarmati, L. and T. Trinh, “Measuring user behavior in online social networks, Network, IEEE, vol. 24(5), pp. 26-31, 2010.
[Kim11]Kim H.-T., Lee J.-H., and Ahn C.-W., “A Recommender System based on interactive evolutionary computation with data grouping, Procedia Computer Science, vol. 3, pp.611-616, 2011.
[Lu11]Lu, Z., Yanlong, Wen, Haiwei, Zhang, Ying Zhang, and Xiaojie, Yuan, “User relationship index based on social network community analysis, Business Management and Electronic Information (BMEI), International Conference on, vol. 4, pp.66-69, 2011.
[Mai08]Maia, M., J. Almeida and V. Almeida, “Identifying user behavior in online social networks, Proceedings of the 1st Workshop on Social Network Systems, Glasgow, Scotland, pp. 1-6, 2008.
[McP01]McPherson M. et al., Birds of a feather: Homophily in social networks, Annual review of sociology, pp. 415-444, 2001.
[Tan11]Tang L. et al., “Community detection via heterogeneous interaction analysis, Data Mining and Knowledge Discovery, pp.1-33, 2011.
[Tur02]Turney, P. D., “Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews, In Processing of 40th Annual Meeting on Association for Computational Linguistics, pp. 417-424, 2002.
[Tur03]Turney, P. D. and M.L. Littman, “Measuring praise and criticism: Inference of semantic orientation from association, ACM Transactions on Information System, vol. 21, pp. 315-346, 2003.
[Vra11]Vrabl, S., J. Oliveira, and C.L.R. Motta, “#twintera!: A social matching environment based on microblogging, Computer Supported Cooperative Work in Design (CSCWD), 15th International Conference on, pp.556-561, 2011.
[Wil09]Wilson, C., Boe, B., Sala, A., Puttaswamy, K.P.N., and Zhao, B.Y., “User interactions in social networks and their implications, Proceedings of the 4th ACM European conference on Computer systems, Nuremberg, Germany, pp. 205-218, 2009.
[Yao11]Yao T. et al., “Context-based Friend Suggestion in Online Photo-sharing Community, MM '11 Proceedings of the 19th ACM international conference on Multimedia, 2011.
[Wik01]Wikipedia - Concept drift, http://en.wikipedia.org/wiki/Concept_drift [retrieved: April, 2012].

[Wik02]Wikipedia - Discounted cumulative gain: http://en.wikipedia.org/wiki/Discounted_cumulative_gain [retrieved: June, 2012].
[Wik03]Wikipedis - Modularity, http://en.wikipedia.org/wiki/Modularity_(networks) [retrieved: May, 2012]
[Onl01]PHOTOGRAPHYTIPS.COM™: http://www.photographytips.com/page.cfm/1587 [retrieved: April, 2012].
[Onl02]Sentiment140: http://www.sentiment140.com/ [retrieved: May, 2012].
[Onl03]SocialMention: http://socialmention.com/ [retrieved: May, 2012].

[Onl04]Twitrratr: http://twitrratr.com/ [retrieved: May, 2012].

連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top