臺灣博碩士論文加值系統

隨著科技不斷的發展，社群媒體的應用也越來越廣泛與普及，在過去大多數的新聞與資訊都是透過傳統的新聞媒體進行傳播、散佈，但是隨著社群媒體的應用普及化，不僅僅是傳統的電子媒體開始更加頻繁的使用社群媒體來傳達最新資訊與新聞。對於企業而言，社群媒體也使得使用者資料更容易的取得，為了解決隱私的問題，許多社群媒體提供了多種隱私權選擇給使用者選擇，就如Facebook上個人主頁並不是能夠完整的提供屬性資料，使用者可以依據喜好將隱私設定為完全公開、指定只有某人能觀看、只對朋友公開或完全不對外公開…等屬性，所以對於企業而言，取得使用者的資料變得更加的困難，從而減少社群媒體的應用性。因此本研究提出一套模型設定，對使用者使用進行識別，以找出使用者的未知屬性。

As continuous development, the application of social media is more and more widely and popular. In the past, most of the news and information through the traditional news media to spread. But through the popularity of community media, not only traditional electronic media began to use more social media to communicate the latest news. For business purposes, community media also makes it easier for users to get information. To address privacy issues, many community media offer multiple options for privacy. For example Facebook's personal home page is not able to provide complete property information, the user can set the privacy to be fully public, depending on the preferences, and only someone can watch, only open to friends or not open to the public ... and other attributes. For the enterprise, the user's information becomes more difficult. It reduced the applicability of community media. Therefore, this study proposed a set of model that are used by the user to identify the unknown attributes of the user.

目次
摘要 i
Abstract ii
目次 iii
表目次 v
圖目次 vi
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究問題 2
1.4 研究限制 2
1.5 研究流程與架構 3
第二章 文獻探討 5
2.1 social media 5
2.2 使用者屬性的推斷 6
2.3 語意分析 7
第三章 研究方法 9
3.1 前處理：CKIP 中文斷詞 10
3.2 特徵詞資料庫 11
3.3 分數計算 11
第四章 實驗案例 13
4.1 背景介紹 14
4.2 資料來源 14
4.3 實驗結果 19
4.4 討論與小結 26
第五章 結論與建議 27
參考文獻 28
一、英文部分 28
二、網路資料 30
 
表目次
表1使用者在各類屬性顯示的比例 20
表2使用者在各類屬性的比例 20
表3嬰幼兒別稱辭庫 22
表4嬰幼兒用品辭庫 23
表5嬰幼兒習俗習慣辭庫 24
表6嬰幼兒地點辭庫 24
表7行為詞庫 25
表8統計表格 25
表9 樣本預测结果 26
表10 混淆矩陣 26

圖目次
圖1研究流程架構與說明 4
圖2 本研究所提研究架構 9
圖3 Graph API 測試工具 15
圖4互動資料定義示意圖 16
圖5雲品溫泉酒店日月潭Fleur de Chine Hotel所蒐集貼文的資料 17
圖6雲品溫泉酒店日月潭Fleur de Chine Hotel所蒐集的回覆資料 18
圖7雲品溫泉酒店日月潭Fleur de Chine Hotel所蒐集的使用者資料 19
圖8 CKIP中文斷詞系統斷詞 21

Acker, O., Gröne, F., Akkad, F., Pötscher, F., & Yazbek, R. (2011). Social CRM: How companies can link into the social web of consumers. Journal of Direct, Data and Digital Marketing Practice, 13(1), 3-10.
Annie Jin, S.-A. (2012). The potential of social media for luxury brand management. Marketing Intelligence & Planning, 30(7), 687-699.
Blazevic, V., Hammedi, W., Garnefeld, I., Rust, R. T., Keiningham, T., Andreassen, T. W., ... & Carl, W. (2013). Beyond traditional word-of-mouth: an expanded model of customer-driven influence. Journal of Service Management,24(3), 294-313.
Bracewell, D. B. (2008, September). Semi-automatic creation of an emotion dictionary using wordnet and its evaluation. In 2008 IEEE Conference on Cybernetics and Intelligent Systems (pp. 1385-1389). IEEE.
Colomo-Palacios, R., & Fernando, I. (2010). Community creation by means of a social media paradigm. The learning organization, 17(6), 500-514.
Davis Jr, C. A., Pappa, G. L., de Oliveira, D. R. R., & de L Arcanjo, F. (2011). Inferring the location of twitter messages based on user relationships. Transactions in GIS, 15(6), 735-751.
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American society for information science, 41(6), 391.
Dholakia, U. M., & Durham, E. (2010). One café chain’s Facebook experiment. Harvard Business Review, 88(3), 26.
Dong, Y., Yang, Y., Tang, J., Yang, Y., & Chawla, N. V. (2014, August). Inferring user demographics and social strategies in mobile social networks. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 15-24). ACM.
Gabrilovich, E., & Markovitch, S. (2007, January). Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In IJcAI (Vol. 7, pp. 1606-1611).
He, J., Chu, W. W., & Liu, Z. V. (2006, May). Inferring privacy information from social networks. In International Conference on Intelligence and Security Informatics (pp. 154-165). Springer Berlin Heidelberg.
Hofmann, T. (2001). Unsupervised learning by probabilistic latent semantic analysis. Machine learning, 42(1-2), 177-196.
Holsing, C., & Olbrich, R. (2012, August). Effect of user-generated content on website stickiness: the case of social shopping communities. In Proceedings of the 14th Annual International Conference on Electronic Commerce (pp. 301-308). ACM.
Hutter, K., Hautz, J., Dennhardt, S., & Füller, J. (2013). The impact of user interactions in social media on brand awareness and purchase intention: the case of MINI on Facebook. Journal of Product & Brand Management, 22(5/6), 342-351.
Jurgens, D. Thats what friends are for: Inferring location in online social media platforms based on social relationships. In: Proc. of the 7th International AAAI Conference on Weblogs and Social Media, pp 273{282, AAAI Press (2013)
Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business horizons, 53(1), 59-68.
Kasavana, M. L., Nusair, K., & Teodosic, K. (2010). Online social networking: redefining the human web. Journal of Hospitality and Tourism Technology, 1(1), 68-82.
Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business horizons, 54(3), 241-251.
King, R. A., Racherla, P., & Bush, V. D. (2014). What we know and don't know about online word-of-mouth: A review and synthesis of the literature. Journal of Interactive Marketing, 28(3), 167-183.
Kroski, E. (2008). Web 2.0 for librarians and information professionals. Neal-Schuman Publishers, Inc..
Laroche, M., Habibi, M. R., & Richard, M. O. (2013). To be or not to be in social media: How brand loyalty is affected by social media?. International Journal of Information Management, 33(1), 76-82.
Li, R., Wang, C., & Chang, K. C. C. (2014, April). User profiling in an ego network: co-profiling attributes and relationships. In Proceedings of the 23rd international conference on World wide web (pp. 819-830). ACM.
Mangold, W. G., & Faulds, D. J. (2009). Social media: The new hybrid element of the promotion mix. Business horizons, 52(4), 357-365.
Mantelero, A., & Vaciago, G. (2014). Chapter 14 - Social media and Big Data. In B. A. S. Bosco (Ed.), Cyber Crime and Cyber Terrorism Investigator's Handbook (pp. 175-195): Syngress.
Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. The journal of marketing, 20-38.
Qualman, E. (2010). Socialnomics: How social media transforms the way we live and do business. John Wiley & Sons.
Quercia, D., Kosinski, M., Stillwell, D., & Crowcroft, J. (2011, October). Our twitter profiles, our selves: Predicting personality with twitter. In Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on (pp. 180-185). IEEE.
Trainor, K. J. (2012). Relating social media technologies to performance: A capabilities-based perspective. Journal of Personal Selling & Sales Management, 32(3), 317-331.
Yan, J., Bracewell, D. B., Ren, F., & Kuroiwa, S. (2008). The Creation of a Chinese Emotion Ontology Based on HowNet. Engineering Letters, 16(1), 166-171.
Yates, D., & Paquette, S. (2011). Emergency knowledge management and social media technologies: A case study of the 2010 Haitian earthquake. International Journal of Information Management, 31(1), 6-13.