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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 25-Aug-2014 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
dc.contributor.advisor 唐揆zh_TW
dc.contributor.advisor Tang, Kweien_US
dc.contributor.author (Authors) 傅思瑜zh_TW
dc.contributor.author (Authors) Fu, Szu Yuen_US
dc.creator (作者) 傅思瑜zh_TW
dc.creator (作者) Fu, Szu Yuen_US
dc.date (日期) 2013en_US
dc.date.accessioned 25-Aug-2014 15:13:11 (UTC+8)-
dc.date.available 25-Aug-2014 15:13:11 (UTC+8)-
dc.date.issued (上傳時間) 25-Aug-2014 15:13:11 (UTC+8)-
dc.identifier (Other Identifiers) G0101355057en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/69177-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 企業管理研究所zh_TW
dc.description (描述) 101355057zh_TW
dc.description (描述) 102zh_TW
dc.description.abstract (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。		zh_TW
dc.description.abstract (摘要) 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.		en_US
dc.description.tableofcontents 目錄
致謝辭 i
摘要 ii
Abstract iii
圖目錄 vi
表目錄 vii
第壹章 緒論 1
第一節 研究背景 1
第二節 研究動機 3
第三節 研究問題 6
第四節 研究架構 7
第貳章 文獻探討 8
第一節 使用者創作內容對購買決策的影響 8
一、 社群參與和購買決策： 8
二、 社會互動與購買決策 9
第二節 產品屬性分類與相對應之產品面向 10
第三節 產品面向萃取與情緒偵測 12
第四節 熱門討論之產品面向彙整 15
一、 以產品評論為分析資料： 15
二、 以社群媒體內容為分析資料： 17
第參章 研究方法 19
第一節 資料描述 19
一、 資料來源與資料形式 19
二、 資料涵蓋期間 22
三、 資料特性描述 23
第二節 資料處理 24
一、 新聞內容之面向標簽 25
二、 讀者回覆內容之面向萃取 26
第肆章 實驗分析與討論 29
第一節 iPhone手機三世代產品描述 29
第二節 原始資料描繪 33
第三節 資料分析與問題討論 36
一、 產品發表前，廣為大眾討論的產品面向分析。 38
二、 產品發表前，各產品面向第一次出現在新聞內容中的先後順序。 40
三、 群眾在產品發表前和購買後，廣為討論之產品面向的異同。 45
第伍章 結論 49
一、 學術貢獻與管理建議 49
二、 後續研究方向 50
參考文獻 52		zh_TW
dc.format.extent 2555112 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0101355057en_US
dc.subject (關鍵詞) 使用者創作內容zh_TW
dc.subject (關鍵詞) 產品面向zh_TW
dc.subject (關鍵詞) 面向萃取zh_TW
dc.subject (關鍵詞) 上市前zh_TW
dc.subject (關鍵詞) User-generated Contenten_US
dc.subject (關鍵詞) product aspectsen_US
dc.subject (關鍵詞) aspect extractionen_US
dc.subject (關鍵詞) pre-launchen_US
dc.title (題名) 產品上市前最被廣為討論的產品面向:以iPhone為例zh_TW
dc.title (題名) Identifying Most-buzzed Product Aspects in Pre-launch Stage: iPhone Case Studyen_US
dc.type (資料類型) thesisen
dc.relation.reference (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.		zh_TW