Opinion Leader Discovery in Dynamic Social Networks

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：31

、訪客IP：3.15.5.183

姓名

陳彥宇(Yen-Yu Chen) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

(Opinion Leader Discovery in Dynamic Social Networks)

相關論文

★ 台灣50走勢分析：以多重長短期記憶模型架構為基礎之預測	★ 以多重遞迴歸神經網路模型為基礎之黃金價格預測分析
★ 增量學習用於工業4.0瑕疵檢測	★ 遞回歸神經網路於電腦零組件銷售價格預測之研究
★ 長短期記憶神經網路於釣魚網站預測之研究	★ 基於深度學習辨識跳頻信號之研究
★ 深度學習模型於工業4.0之機台虛擬量測應用	★ A Novel NMF-Based Movie Recommendation with Time Decay
★ 以類別為基礎sequence-to-sequence模型之POI旅遊行程推薦	★ A DQN-Based Reinforcement Learning Model for Neural Network Architecture Search
★ Neural Network Architecture Optimization Based on Virtual Reward Reinforcement Learning	★ 生成式對抗網路架構搜尋
★ 以漸進式基因演算法實現神經網路架構搜尋最佳化	★ Enhanced Model Agnostic Meta Learning with Meta Gradient Memory
★ 遞迴類神經網路結合先期工業廢水指標之股價預測研究	★ A Novel Reinforcement Learning Model for Intelligent Investigation on Supply Chain Market

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

社群網路為由於其廣泛的實用性而引起了研究者的關注。開發了幾種技術用於從社群網路之使用者的規律中探勘有用的知識。意見領袖發現是一項具有重大商業和政治價值的重要任務。通過找尋意見領袖，公司或政府可以分別利用其強大之影響力進行銷售活動或指導公眾輿論。此外，檢測有影響力的評論能夠理解輿論形成的來源和趨勢。然而，之前的研究主要集中在尋找靜態社群網路中的意見領袖，較少考慮社群網路隨時間演進的影響。在實際應用中，社群網路通常隨著時間的推移而演變，在動態社交網絡中尋找意見領袖的研究工作很少且具備難度。在本文中，提出了一種新的尋找意見領袖方法: DOLM，以有效地從動態社群網路中識別意見領袖。我們首先構建動態社群網路，然後檢測社區結構以解決資訊重疊問題。然後，DOLM開發基於分群的領導力分析，以找出動態社群網路中的意見領袖。實驗研究表明，該方法能夠有效捕捉動態社群網路的特徵，解決資訊重疊問題。最後我們還在幾個真實資料集上應用DOLM，以檢視意見領袖探勘的效率和擴展性。

摘要(英)

Social network analysis has attracted researchers’ attention due to its widespread practicability. Several techniques are developed for extracting useful knowledge from users’ regularities. Opinion leader discovery is one essential task which has great commercial and political values. By identifying the opinion leaders, companies or governments could manipulate the selling or guiding public opinion, respectively. Additionally, detecting the influential comments is able to understand the source and trend of public opinion formation. However, prior studies mainly focus on finding opinion leader in a static social network. Actually, in real applications, social networks are usually evolved with time; few research efforts have been elaborated on finding opinion leaders in dynamic social network. In this study, a novel algorithm, DOLM, is proposed to efficiently find the opinion leaders from a dynamic social network. We utilize a network emerging method to construct a dynamic social network, and then detect the community structure to tackle the information overlapping problem. Then, DOLM develops a clustering-based leadership analysis to find out the opinion leader in a dynamic social network. The experimental study shows that the proposed algorithm could effectively capture the characteristic of a dynamic social network and solve the information overlapping problem. We also apply DOLM on several real datasets to show the efficiency and scalability for opinion leader discovery.

關鍵字(中)

★ 意見領袖
★ 社群網路

關鍵字(英)

★ Opinion Leader
★ Social Networks

論文目次

中文提要 ……………………………………………………………… i
英文提要 ……………………………………………………………… ii
誌謝 ……………………………………………………………… iii
目錄 ……………………………………………………………… iv
圖目錄 ……………………………………………………………… v
表目錄 ……………………………………………………………… vi
符號說明 ……………………………………………………………… vii

1. Introduction………………………………………………… 1
2. Preliminary………………………………………………… 3
3. Related Work……………………………………………… 4
3.1 Opinion Analysis and Mining……………4
3.2 Opinion Leader ………………………………………… 5
3.3 Opinion Leader Mining……………………………5
3.4 Community Structure…………………………………5
4. DOLM Algorithm…………………………………………… 6
4.1 Dynamic Social Network Construction and Aggregation…… 7
4.2 Community Structure Detection………8
4.3 Candidate Generation……………………………11
4.4 Leader Selection………………………………………13
5. Experiments……………………………………………………14
5.1 Real Dataset Collection……………………14
5.2 Experiments and Discussion……………15
6. Conclusion…………………………………………………… 21
References ……………………………………………………………22

參考文獻

[1] E. Katz, “The two-step flow of communication: An up-to-date report on a hypothesis”, Public opinion quarterly, vol. 21, no. 1, pp. 61-78, 1957.
[2] F. Bodendorf, and C. Kaiser. “Detecting Opinion Leaders and Trends in Online Social Networks”, Proceedings of the 2nd ACM workshop on Social web search and mining, pp. 65-68, 2009.
[3] Y. Cho, J. Hwang, and D. Lee. “Identification of Effective Opinion Leaders in the Diffusion of Technological Innovation: A Social Network Approach”, Technological Forecasting and Social Change, vol. 79, no.1, pp. 97-106, 2012.
[4] J. Duan, J. Zeng, and B. Luo. “Identification of Opinion Leaders Based on User Clustering and Sentiment Analysis”, Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), pp. 377-383, 2014.
[5] F. Li, and T. Du, “Who is Talking? an Ontology-Based Opinion Leader Identification Framework for Word-of-Mouth Marketing in Online Social Blogs”, Decision Support Systems, vol. 51, no. 1, pp. 190-197, 2011.
[6] Y. Li, S. Ma, Y. Zhang, R. Huang, “An Improved Mix Framework for Opinion Leader Identification in Online Learning Communities”, Knowledge-Based Systems, vol. 43, pp. 43-51, 2013.
[7] Q. Miao, S. Zhang, Y. Meng, and H. Yu, “Domain-Sensitive Opinion Leader Mining from Online Review Communities”, Proceedings of the 22nd international conference on World Wide Web companion, pp. 187-188, 2013.
[8] K. Song, D. Wang, S. Feng, and G. Yu, “Detecting Opinion Leader Dynamically in Chinese News Comments”, Web-Age Information Management.Springer, pp. 197-209, 2012.
[9] X. Yu, X. Wei, and X. Lin. “Algorithms of BBS Opinion Leader Mining Based on Sentiment Analysis”, Web Information Systems and Mining.Springer, pp. 360-369, 2010.
[10] H. Zhou, D. Zeng, and C. Zhang. “Finding Leaders from Opinion Networks”, Intelligence and Security Informatics, 2009. ISI′09., pp. 266-268, 2009.
[11] Z. Zhai, H. Xu, and P. Jia. “Identifying Opinion Leaders in BBS”, IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT′08., vol. 3, pp.398-401, 2008.
[12] B. Liu, and L. Zhang. “A Survey of Opinion Mining and Sentiment Analysis”, Mining Text Data, pp. 415-463, 2012.
[13] B. Liu, M. Hu, and J. Cheng, “Opinion Observer: Analyzing and Comparing Opinions on the Web”, Proceedings of the 14th international conference on World Wide Web, pp. 342-351, 2005.
[14] L. Ku, H. Ho, and H. Chen, “Opinion Mining and Relationship Discovery using CopeOpi Opinion Analysis System”, Journal of the American Society for Information Science and Technology, vol. 60, no. 7, pp. 1486-14503, 2009.
[15] Princeton University “About WordNet.” WordNet. Princeton University. 2010. http://wordnet.princeton.edu.
[16] General Inquire. http://www.wjh.harvard.edu/~inquirer/.
[17] HowNet. http://www.keenage.com/html/e_index.html.
[18] L. Ku, and H. Chen, “Mining Opinions from the Web: Beyond Relevance Retrieval”, Journal of the American Society for Information Science and Technology, vol. 58, no. 12, pp. 1838-1850, 2007.
[19] Newman,M. E. J. (2006). "Modularity and community structure in networks". Proceedings of the National Academy of Sciences of the United States of America. 103 (23):8577–8582., 2006.
[20] Z. Feng, X. Xu, N. Yuruk, and T. Schweiger, “A novel similarity-based modularity function for graph partitioning”, In Proceedings of the 9th International Conference on Data Warehousing and Knowledge Discovery (DaWaK’07), pp. 385–396, 2007.
[21] Y. Chen, W. Zhu, W. Peng, W. Lee, and S. Lee. “CIM: community-based influence maximization in social networks”, ACM Transactions on Intelligent Systems and Technology (TIST), vol. 5, no.2, pp. 25, 2014.
[22] L.Wan, J. Liao, and X. Zhu, “Finding evaluating community structure in social networks”, In Proceedings of the 4th International Conference on Advanced Data Mining and Applications (ADMA’08), pp. 620–627, 2008.
[23] Mobile01. http://www.mobile01.com
[24] P. F. Lazarsfeld, The people′s choice: how the voter makes up his mind in a presidential campaign, New York, 1944.
[25] H. Sharara, L. Getoor, and M. Norton, Active surveying: aprobabilistic approach for identifying key opinion leaders, inProceedings of the Twenty-Second international joint conference on Artificial Intelligence Barcelona, Catalonia, Spain, 2011, pp. 1485-1490.
[26] E. Rogers, Diffusion of Innovations, 3 ed., New York: Free Press, 1983.

指導教授

陳以錚(Yi-Cheng Chen)

審核日期

2019-1-28

推文