(18.207.129.82) 您好!臺灣時間:2021/04/19 19:31
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:::

我願授權國圖
: 
twitterline
研究生:高聖倫
研究生(外文):Sheng-LunKao
論文名稱:以圖文特徵為基之消費者購買決策型態辨識方法研發
論文名稱(外文):Development of Method for Graphic and Text Features based Consumer Decision-Making Style Identification
指導教授:陳裕民陳裕民引用關係
指導教授(外文):Yuh-Min Chen
學位類別:碩士
校院名稱:國立成功大學
系所名稱:製造資訊與系統研究所
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:2018
畢業學年度:105
語文別:中文
論文頁數:67
中文關鍵詞:消費者購買決策型態臉書影像分析機器學習群眾外包
外文關鍵詞:Consumer decision-making stylesFacebookImage analysisMachine learningCrowdsourcing
相關次數:
  • 被引用被引用:0
  • 點閱點閱:69
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
當今企業競爭激烈的環境中,行銷策略擬定已成為企業成敗的重要關鍵因素,而企業是否瞭解消費者之購買決策型態是行銷策略所不可忽略。以往企業為了解消費者購買決策型態在大量問卷施測以及統計分析上費時費力。隨著網際網路發達與社群媒體普及,越來越多使用者會在社群媒體上留下文字、相片等資料,並且從中揭露了自身的資訊。目前最主要的社群網站臉書,每天有數以億計的內容被分享,這也提供了企業可以客觀地暸解消費者購買決策型態。
本研究目的在於使用青年臉書使用者之「文章」與「相片」作為資料來源,以主題與情感特徵為線索,發展一消費者購買決策型態辨識方法,以供企業分析消費者購買決策型態,進而訂定有效的行銷策略、提高競爭力。針對上述目的,本研究主要研究項目包括:(i)圖文特徵為基之購買決策型態辨識方法設計,(ii)圖文特徵擷取方法發展,(iii)以消費者購買決策型態分類方法發展,(iv)系統實作與驗證。
本研究使用Sproles and Kendall提出之消費者購買決策模型,來辨識臉書使用者的消費者購買決策型態,透過群眾外包之資料標註,運用影像分析技術分析相片內容、文字探勘技術分析文字內容、機器學習技術將使用者之消費者購買決策型態進行分類。
本研究提出以圖文特徵為基之消費者購買決策型態辨識方法之實驗結果,經5-fold cross validation,驗證本研究提出之辨識方法,顯示8種購買決策型態,分別有40%~63%之準確度,其中最高之準確度為第四種購買決策型態:娛樂-快樂主義導向,經本研究提出之文章、相片辨識方法,皆為近63%之準確度。而圖文整合之準確度相較僅用文章或相片的辨識方法略有提升。
In today's competitive environment, the formulation of marketing strategy has become an important key factor in the success or failure of enterprises, and whether the enterprise is aware of consumer decision-making styles is that a marketing strategy should not be ignored.It is time-consuming and laborious that enterprises spent a lot on a large number of questionnaire surveys and statistical analysis to understand consumer decision-making styles in the past.With the development of Internet and the popularity of community media, more and more users will leave text, graphics and other data in the community media, and from which to expose their own information.At present, in the most important community website - Facebook (Wikipedia. 2016), there are hundreds of millions of content to be shared every day, which also provides an enterprise can objectively understand consumer decision-making styles.
The aim of this study is to use the text and graphics of young Facebook users as as the data source, with themes and emotional characteristics for clues, to develop a method of consumer decision-making style identificationa for the enterprise’s analysis of consumer decision-making style, and then the development of an effective marketing strategy to improve competitiveness.For the above purposes, the main research projects of this study include: (i) the design of method for graphic and text features based consumer decision-making style identification, (ii) the development of method for graphic and text feature extraction, (iii) the development of method for the classification of consumer decision-making style, (iv) the system’s implementation and validation.
In this study, we use the model of consumer decision-making styles proposed by Sproles and Kendall to identify the consumers' decision-making styles of Facebook users. In addition, through crowdsourcing’s data label, we use the image analysis technique to analyze the content of the graphics, the text mining technique to analyze the text content, and the machine learning technique to classify the consumer decision-making styles.
In this study, the experimental results of the method for graphic and text features based consumer decision-making style identification are presented. Through five-fold cross validation, the identification method proposed in this study have shown that eight kinds of decision-making styles had 40%~63% accuracy respectively. The highest accuracy of decision-making styles is the fourth style: recreational and hedonistic whose text and graphics’s identifications proposed by this study are nearly 63% of the accuracy. And the accuracy of the integration of graphic and text compared with only text or graphics identification method is slightly improved.
摘要 I
Extended Abstract II
誌謝 XII
目錄 XIII
表目錄 XV
圖目錄 XVI
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究目的 2
1.4 研究問題分析 3
1.5 研究項目與方法 4
1.6 研究發展程序 6
第二章 文獻探討 7
2.1 研究領域探討 7
2.2 應用方法探討 10
2.3 相關技術探討 11
2.4 類似研究 19
第三章 以圖文特徵為基之消費者購買決策型態辨識方法設計 21
3.1 以圖文特徵為基之消費者購買決策型態辨識方法之概念設計 21
3.2 以圖文特徵為基之消費者購買決策型態辨識之架構設計 22
第四章 消費者購買決策型態辨識方法開發 25
4.1 受測者購買決策型態評量 25
4.2 受測者圖文特徵分析 30
4.3 受測者購買決策型態之圖文特徵模型建立 45
4.4 使用者圖文特徵分析 46
4.5 使用者購買決策型態辨識 49
第五章 方法實作與驗證 52
5.1 實作環境 52
5.2 實作結果 53
5.2.1 資料收集 53
5.2.2 資料輪廓 56
5.3 實驗結果 59
5.3.1 分類器實驗結果 59
5.3.2 以圖文特徵為基之消費者購買決策型態辨識之實驗結果 60
5.4 結果與討論 61
第六章 結論與未來展望 62
6.1 結論 62
6.2 未來研究方向 63
參考文獻 64
[1] Babin, Barry J.; Darden, William R.; Griffin, Mitch. Work and/or fun: measuring hedonic and utilitarian shopping value. Journal of consumer research, 1994, pp. 644-656.
[2] Batra, Satish K., S. H. H. Kazmi, and Satish K. Batra. Consumer Behaviour-2nd. Excel Books India, pp.375 2009.
[3] Bakewell, Cathy, and Vincent-Wayne Mitchell. Generation Y female consumer decision-making styles. International Journal of Retail & Distribution Management , 2003,pp. 95-106.
[4] Bettman, James R. Perceived risk and its components: A model and empirical test. Journal of marketing research, 1973, pp. 184-190.
[5] Chen, Y., Zhou, X. S., & Huang, T. S. (2001). One-class SVM for learning in image retrieval. Paper presented at the Image Processing, 2001. Proceedings. 2001 International Conference on.
[6] Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001). Active appearance models. IEEE Transactions on pattern analysis and machine intelligence, 23(6), 681-685.
[7] Doan A., Ramakrishnan R., and Halevy A. Y., Crowdsourcing Systems on the World-Wide Web. Communications of the ACM, Vol. 54 No. 4, p.86-96. (2011).
[8] Dougherty, James & Kohavi, Ron & Sahami, Mehran. (1997). Supervised and Unsupervised Discretization of Continuous Features. ICML. 1995. . 10.1016/B978-1-55860-377-6.50032-3.
[9] Edwards, J., Jackson, H. J., & Pattison, P. E. (2002). Emotion recognition via facial expression and affective prosody in schizophrenia: a methodological review. Clinical psychology review, 22(6), 789-832.
[10] Ekman, P., Rolls, E., Perrett, D., & Ellis, H. (1992). Facial expressions of emotion: An old controversy and new findings [and discussion]. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 335(1273), 63-69.
[11] Hafstrom, Jeanne L.; Chae, Jung Sook; Chung, Young Sook. Consumer decision‐making styles: comparison between United States and Korean young consumers. Journal of Consumer Affairs, 1992, pp. 146-158.
[12] Hinde, R. A. (1972). Non-verbal communication: Cambridge University Press.
[13] H. J. Escalante, M. Montes-y-Goméz, and L. E. Sucar, An energy-based model for region-labeling, Computer Vision and Image Understanding, vol. 115, pp. 787-803, 2011.
[14] Hoerl, Arthur E., and Robert W. Kennard. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics ,1970, pp. 55-67.
[15] Horner, Susan, and John Swarbrooke. Consumer behaviour in tourism. Routledge, 2016.
[16] H.-T. Chang, N. Mastorakis, V. Mladenov, Z. Bojkovic, D. Simian, S. Kartalopoulos, A. Varonides, C. Udriste, E. Kindler, and S. Narayanan, Automatic web image annotation for image retrieval systems, in WSEAS International Conference. Proceedings. Mathematics and Computers in Science and Engineering, 2008.
[17] Huang, C.-L., & Huang, Y.-M. (1997). Facial expression recognition using model-based feature extraction and action parameters classification. Journal of Visual Communication and Image Representation, 8(3), 278-290.
[18] J. Golbeck, C. Robles, M. Edmondson and K. Turner, Predicting Personality from Twitter, 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, Boston, MA, 2011, pp. 149-156.
[19] Kohavi, Ron (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence 2 (12): 1137–1143.(Morgan Kaufmann, San Mateo).
[20] Kosinski, M., Stillwell, D. and Graepel, T., Private traits and attributes are predictable from digital records of human behavior, Proceedings of the National Academy of Sciences, Vol. 110, No. 15, 2013, pp. 5802-5805.
[21] Lai Albert Wenben. Consumer values, product benefits and customer value: a consumption behavior approach. NA-Advances in Consumer Research Volume 22, 1995.
[22] Lajevardi, S. M., & Wu, H. R. (2012). Facial expression recognition in perceptual color space. Image Processing, IEEE Transactions on, 21(8), 3721-3733.
[23] Lee, Eunsun; AHhn, Jungsun; Kim, Yeo Jung. Personality traits and self-presentation at Facebook. Personality and Individual Differences, 2014, pp. 162-167.
[24] Lysonski, Steven, Srini Durvasula, and Yiorgos Zotos. Consumer decision-making styles: a multi-country investigation. European journal of Marketing , 1996, pp.10-21.
[25] Ratliff, M. S., & Patterson, E. (2008). Emotion recognition using facial expressions with active appearance models. Paper presented at the Proceedings of the Third IASTED International Conference on Human Computer Interaction,(Innsbruck, Austria).
[26] R. C. Wong and C. H. Leung, Automatic semantic annotation of real-world web images, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 30, pp. 1933-1944, 2008.
[27] Shun-ichi Amari, Natural Gradient Works Efficiently in Learning, Neural computation, 1998, pp. 1-36.
[28] Sprotles, George B.; Kendall, Elizabeth L. A methodology for profiling consumers' decision‐making styles. Journal of Consumer Affairs, 1986, pp. 267-279.
[29] S. Zhu and Y. Liu, Semi-Supervised Learning Model Based Efficient Image Annotation, Signal Processing Letters, IEEE, vol. 16, pp. 989-992, 2009.
[30] Tibshirani, Robert. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) , 1996, pp. 267-288.
[31] Tsung-Yi Chen, Meng-Che Tsai, Yuh-Min Chen, (2016) A user’s personality prediction approach by mining network interaction behaviors on Facebook, Online Information Review, Vol. 40 Issue: 7, pp.913-937.
[32] Vapnik, V. (2000). The nature of statistical learning theory: springer.
[33] Vonikakis, V., & Winkler, S. (2012). Emotion-based sequence of family photos. Paper presented at the Proceedings of the 20th ACM international conference on Multimedia.
[34] Wen-Hung Liao, Ting-Ya Chang, Yi-Chieh Wu, (2017)
Identifying user profile using Facebook photos, International Conference on Ubiquitous Information Management and Communication, pp.1-7.
[35] Xiao, R., Zhao, Q., Zhang, D., & Shi, P. (2011). Facial expression recognition on multiple manifolds. Pattern Recognition, 44(1), 107-116.
[36] Y. Mori, H. Takahashi, and R. Oka, Image-to-word transformation based on dividing and vector quantizing images with words, in First International Workshop on Multimedia Intelligent Storage and Retrieval Management, 1999.
[37] Youyou, W., Kosinski, M. and Stillwell, D., Computer-based personality judgments are more accurate than those made by humans, Proceedings of the National Academy of Sciences, Vol. 112, No, 4, 2015, pp. 1036-1040.
[38] Y. Zhao, Y. Zhao, and Z. Zhu, TSVM-HMM: Transductive SVM based hidden Markov model for automatic image annotation, Expert Systems with Applications, vol. 36, pp. 9813-9818, 2009.
[39] Z. Gong, Q. Liu, and J. Zhang, Automatic image annotation by mining the web, in Data Warehousing and Knowledge Discovery, ed: Springer, 2006, pp. 449-458.
[40] Zhou, X., Shi, Y., Zhang, P., Nie, G., & Jiang, W. (2009). A new classification method for PCA-based face recognition. Paper presented at the Business Intelligence and Financial Engineering, 2009. BIFE'09. International Conference on.
[41] Z. Li, Z. Shi, X. Liu, and Z. Shi, Modeling continuous visual features for semantic image annotation and retrieval, Pattern Recognition Letters, vol. 32, pp. 516-523, 2011.
[42] 楊鵬穎, 影像情感辨識技術研發, 國立成功大學, 2014, pp. 1-43.
[43] 許又壬, Facebook數位足跡為基之消費者購買決策型態預測方法發展, 國立成功大學, 2015, pp. 1-50.
[44] 劉嘉倩, 基於RSS Feed之中文部落格文章分類系統,國立交通大學,2010, pp.1-50.
[45] 高聖傑, 詞性組合輔助之中文網路口碑評價分析技術研發,國立成功大學,2013, pp.1-67.
[46] CKIP中文斷詞系統,http://ckipsvr.iis.sinica.edu.tw/ .
[47] Computer Vision API,https://azure.microsoft.com/zh-tw/services/cognitive-services/computer-vision/ .
[48] Emotion API,https://azure.microsoft.com/en-us/services/cognitive-services/emotion/ .
[49] Engel, J. F., Kollat, D. T., & Roger, D. (1973). Blackwell, consumer behavior. New York: Holt, Rinehart, and Winston.
[50] Howe J., The Rise of Crowdsourcing. In Wired Magazine 14(6). http://archive.wired.com/wired/archive/14.06/crowds_pr.html . (2006).
[51] K Kotler, Philip.; Kevin Lane Keller (2006). Marketing Management, 12th ed. Pearson Prentice Hall. ISBN 0-13-145757-8.
[52] Middleton, V.T.C. and Clarke, J. Marketing for Travel and Tourism, 3rd edn. Butterworth-Heinemann, Oxford., 2001.
[53] September 19, 2013 - by MarketingCharts staff, What Internet Users Like to Share on Social Media Sites, Ipsos is a global market research and a consulting firm with worldwide headquarters in Paris, France., http://www.marketingcharts.com/online/what-internet-users-like-to-share-on-social-media-sites-36804/ .
[54] 林清河,施坤壽,許家銘,『消費者決策型態與價值觀之研究─臺灣地區大學生之實證研究』, 行政院國家科學委員會研究彙刊:人文及社會科學, 2001,第11(1)卷: pp. 16~29.
[55] 蔡孟勳,群眾外包:從太陽花學運到美國矽谷都在用的經營模式。有物報告。取自:https://yowureport.com/從太陽花學運到終極警探-談群眾外包(crowdsourcing)/。(2014) .
[56] 陳伶志與余孝萱,淺談群眾外包—以Amazon Mechanical Turk為例。中央研究院電子週報第309期。取自:http://newsletter.sinica.edu.tw/news/read_news.php?nid=5613。(2011) .
[57] 數位影像處理 (Digital Image Processing, 3/e),Rafael C. Gonzalez, Richard E. Woods 著、繆紹綱 譯,出版商:GL高立, ISBN-13:9789866534102 .
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
系統版面圖檔 系統版面圖檔