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    政大機構典藏 > 商學院 > 企業管理學系 > 學位論文 >  Item 140.119/69177


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    題名: 產品上市前最被廣為討論的產品面向:以iPhone為例
    Identifying Most-buzzed Product Aspects in Pre-launch Stage: iPhone Case Study
    作者: 傅思瑜
    Fu, Szu Yu
    貢獻者: 唐揆
    Tang, Kwei
    傅思瑜
    Fu, Szu Yu
    關鍵詞: 使用者創作內容
    產品面向
    面向萃取
    上市前
    User-generated Content
    product aspects
    aspect extraction
    pre-launch
    日期: 2013
    上傳時間: 2014-08-25 15:13:11 (UTC+8)
    摘要: 近年來使用者創作內容受到廣泛的重視，其對大眾的影響力讓社群網路與產品評論網站上各種形式的發表內容都成為學者研究的對象。與產品相關的使用者創作內容，依發表的時間點，大致可分為產品上市前的討論和產品評論兩種。目前針對面向萃取的研究多以線上產品評論為分析資料，然而對廠商而言，若以此資料萃取出的產品面向作為行銷訊息的主題，則可能忽略了消費者在購買產品前後所在意的產品面向可能有所不同的情形。

    在產品上市前的猜測、討論或謠言(buzzes or rumors)通常反映出群眾對產品面向的期待，本研究以此為分析資料，並從中找出產品在發表前被熱烈討論的產品面向。研究發現不同於產品評論資料，產品上市前的討論中，和功能無關的面相如售價、上市日期、手機外殼材質和顏色等，都是群眾關注的焦點。實驗結果讓廠商更能掌握大眾在實際接觸產品前最在意的產品面向，亦可在行銷產品時更有效地製造話題與達到吸引關注的目的。
    User-generated content (UGC) has drawn much attention in recent years and researchers study all forms of UGC because of its huge impact. According to the time when UGC is produced, there are two major types of product-related UGC: pre-launch buzzes and product reviews. The previous studies on product aspect extraction mainly use online product reviews as research dataset. However, forming marketing message only on the basis of these aspects might neglect the fact that people focus on different aspects before their purchase.

    Prediction, buzzes and rumors in pre-launch stage usually confer the expectation of product aspects. Using product-related UGC in pre-launch stage as dataset, this paper aims to identify the most buzzed product aspects before a product is even launched. Unlike the result extracted from product reviews, people frequently buzz about non-functional aspects such as price, release date, and color and material of mobile phone case in pre-launch stage. Firms can see the findings as a reference while formulating marketing message. By keeping track of these aspects, marketing practitioners could create buzzes and promote new a product more efficiently.
    參考文獻: Alfred, S.B. (1981). Market Segmentation by Personal values and Salient Product Attributes. Journal of Advertising Research, Vol.21, No.1, pp.29-35.
    Archak, N., Ghose, A., & Ipeirotis, P. G. (2011). Deriving the pricing power of product features by mining consumer reviews. Management Science, 57(8), 1485-1509.
    Blair-Goldensohn, S., Hannan, K., McDonald, R., Neylon, T., Reis, G. A., & Reynar, J. (2008). Building a sentiment summarizer for local service reviews. World Wide Web Workshop on NLP in the Information Explosion Era (p. 14).
    Chen, L., Qi, L., & Wang, F. (2012). Comparison of feature-level learning methods for mining online consumer reviews. Expert Systems with Applications, 39(10), 9588-9601.
    Chen, Y., Wang, Q., & Xie, J. (2011). Online social interactions: A natural experiment on word of mouth versus observational learning. Journal of Marketing Research, 48(2), 238-254.
    Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of marketing research, 43(3), 345-354.
    Decker, R., & Trusov, M. (2010). Estimating aggregate consumer preferences from online product reviews. International Journal of Research in Marketing, 27(4), 293-307.
    Dellarocas, C., Zhang, X. M., & Awad, N. F. (2007). Exploring the value of online product reviews in forecasting sales: The case of motion pictures. Journal of Interactive marketing, 21(4), 23-45.
    Duan, W., Gu, B., & Whinston, A. B. (2008). Do online reviews matter?—An empirical investigation of panel data. Decision Support Systems, 45(4), 1007-1016.
    Duric, A., & Song, F. (2012). Feature selection for sentiment analysis based on content and syntax models. Decision Support Systems, 53(4), 704-711.
    Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19(3), 291-313.
    Ghose, A., & Ipeirotis, P. G. (2011). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. Knowledge and Data Engineering, IEEE Transactions on, 23(10), 1498-1512.
    Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23(4), 545-560.
    Gu, B., Park, J., & Konana, P. (2012). Research note-the impact of external word-of-mouth sources on retailer sales of high-involvement products. Information Systems Research, 23(1), 182-196.
    Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 168-177), Seattle, WA, USA.
    Jin, W., & Ho, H. H. (2009, June). A novel lexicalized HMM-based learning framework for web opinion mining. Proceedings of the 26th Annual International Conference on Machine Learning (pp. 465-472), Montreal, QC, Canada.
    Jonas, J. R. O. (2010). Source Credibility of Company-produced and User-Generated Content on the Internet: An Exploratory Study on the Filipino Youth. Philippine Management Review, 17.
    Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based brand equity. The Journal of Marketing, 1-22.
    Kotler, P. and Armstrong, G. (1997) Marketing: An Introduction. Fourth Edition. New Jersey. Prentince Hall International
    Ku, L. W., Liang, Y. T., & Chen, H. H. (2006). Opinion Extraction, Summarization and Tracking in News and Blog Corpora. AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs (Vol. 100107).
    Liu, B. (2009). Handbook Chapter: Sentiment Analysis and Subjectivity. New York, NY, USA : Handbook of Natural Language Processing.Marcel Dekker, Inc.
    Liu, B., Hu, M., & Cheng, J. (2005) Opinion observer: analyzing and comparing opinions on the Web. Proceedings of the 14th international conference on World Wide Web, (pp. 342-351), Chiba, Japan.
    Ma, T., & Wan, X. (2010). Opinion target extraction in Chinese news comments. Proceedings of the 23rd International Conference on Computational Linguistics: Posters (pp. 782-790), Stroudsburg, PA, USA.
    Mukherjee, A. & Liu, B. (2012). Aspect extraction through semi-supervised modeling. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers, Jeju Island, Korea.
    Otto, J., D. Sanford, M. Chuang. (2009). Using Hotel Web Ratings Data: Understanding Customers` Overall Satisfaction Ratings. Tourism Review International. 13(1) 51-63
    Popescu, A. M., & Etzioni, O. (2007). Extracting product features and opinions from reviews. Natural language processing and text mining (pp. 9-28). Springer London.
    Qiu, G., Liu, B., Bu, J., & Chen, C. (2011). Opinion word expansion and target extraction through double propagation. Computational linguistics, 37(1), 9-27.
    Rishika, R., Kumar, A., Janakiraman, R., & Bezawada, R. (2013). The effect of customers` social media participation on customer visit frequency and profitability: an empirical investigation. Information systems research, 24(1), 108-127.
    Sashi, C. M. (2012). Customer engagement, buyer-seller relationships, and social media. Management decision, 50(2), 253-272.
    Somasundaran, S., & Wiebe, J. (2009). Recognizing stances in online debates. Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1-Volume 1 (pp. 226-234).
    Sprague, R., & Wells, M. E. (2010). Regulating online buzz marketing: Untangling a web of deceit. American Business Law Journal, 47(3), 415-454.
    Tsai, L. F. (2011). Analysis of Internet Word-of-Mouth of Smartphone in Taiwan–Using Blogs and Forums as Examples. International Journal of Digital Content Technology and its Applications, 5(10), 364-371.
    Tuarob, S., & Tucker, C. S. (2013). Fad or here to stay: Predicting product market adoption and longevity using large scale, social media data. ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. V02BT02A012-V02BT02A012), Portland, OR, USA.
    Wang, L., Youn, B. D., Azarm, S., & Kannan, P. K. (2011). Customer-driven product design selection using web based user-generated content. ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. 405-419), Washington, DC, USA.
    Wu, Y., Zhang, Q., Huang, X., & Wu, L. (2009). Phrase dependency parsing for opinion mining. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3 (pp. 1533-1541), Singapore.
    Yi, J., Nasukawa, T., Bunescu, R., & Niblack, W. (2003). Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. Data Mining, 2003. ICDM 2003. Third IEEE International Conference on Data Mining (pp. 427-434), Melbourne, Florida, USA.
    Yu, J., Zha, Z., Wang, M., Chua, T. Aspect ranking: identifying important product aspects from online consumer reviews. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, OR, USA.
    Yubo Chen, Qi Wang, Jinhong Xie (2011) Online Social Interactions: A Natural Experiment on Word of Mouth Versus Observational Learning. Journal of Marketing Research: Vol. 48, No. 2, pp. 238-254.
    Zhai, Z., Liu, B. Xu, H. and Jia, P. (2010). Grouping Product Features Using Semi-Supervised Learning with Soft-Constraints. Proceedings of International Conference on Computational Linguistics (COLING).
    Zhang, W., Xu, H., & Wan, W. (2012). Weakness Finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis. Expert Systems with Applications, 39(11), 10283-10291.
    Zhuang, L., Jing, F., & Zhu, X. Y. (2006). Movie review mining and summarization. Proceedings of the 15th ACM international conference on Information and knowledge management (pp. 43-50), Arlington, VA, USA.
    描述: 碩士
    國立政治大學
    企業管理研究所
    101355057
    102
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0101355057
    資料類型: thesis
    顯示於類別:[企業管理學系] 學位論文

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