博碩士論文 108423034 詳細資訊




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:56 、訪客IP:18.191.154.174
姓名 鄭筠叡(Yun-Rui Zheng)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 運用社群媒體貼文預測使用者之購物傾向
(Use of Social Media Posts to Predict User Shopping Orientation)
相關論文
★ 運用資料探勘法探討台灣老年人口全民健保醫療資源利用之研究★ 運用地理資訊系統與資料探勘技術於基層診所選址分析與研究─以台北市為例
★ 以醫師觀點探討看診輔助系統建置之研究★ 以創新抗拒觀點探討消費者對客服機器人使用意圖之研究
★ 網路拍賣頁面相關的服務品質 對賣家經營績效之影響★ 多重商品類別的線上再購行為預測模型
★ 以使用與滿足理論與科技接受模式探討人機介面對網購意願之影響★ 整合網路口碑之個人化醫療院所推薦系統-以牙醫診所為例
★ 網路口碑影響智慧型手機銷售量的時間動態分析★ 運用資料探勘技術於建置招生 決策支援系統之研究
★ 評估臨床決策支援系統對候診時間與 醫病關係之影響★ 高等教育招生決策支援系統建構之研究
★ 以社會網路分析觀點探討巨量資料在健康保健領域之研究發展★ 醫療App人機互動設計對使用者滿意度之研究
★ 社群媒體粉絲頁經營之研究─ 以Facebook某健康粉絲頁為例★ 基於網路口碑與醫療利用理論之混合式推薦系統
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]
  1. 本電子論文使用權限為同意立即開放。
  2. 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。
  3. 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) 網路社群媒體的普及提供了消費者一個抒發消費體驗、交換對產品與服務意見的便利平台,也提供企業了解消費者心態的極佳管道。本研究運用機器學習方法分析社群媒體Instagram使用者之貼文資料,建構一套預測使用者購物傾向的模型; 使用者貼文資料包括貼文圖片、貼文內容和貼文特徵等三種類型,購物傾向類型包括市場行家、享樂購物、比較購物、物質主義、衝動購物等五種。本研究首先以問卷調查方式分析147位Instagram使用者之購物傾向,接著以隨機森林 (Random Forest)、決策樹 (Decision Trees)、支援向量機 (Support Vector Machine) 等六種機器學習演算法分析受測者的Instagram貼文資料,最後由模型來判斷受測者之購物傾向。研究結果顯示,預測模型的分類準確率介於72.3%-89.5%,具有良好之判斷能力。本研究成果有有助於企業規劃社群行銷與個人化之產品推薦。
摘要(英) The popularity of online social media has provided consumers with convenient platforms on which they can share their consumption experiences and exchange opinions on products and services and also provided businesses with excellent channels through which they can understand the mentality of consumers. This study employed machine learning to analyze user posts on the social media, Instagram, to construct a user shopping orientation prediction model. The user post data included post image, post content, and post characteristics. The shopping orientation categories included market maven, hedonic shopping, comparison shopping, materialism, and impulse buying. We first investigated the shopping orientations of 147 Instagram users using a questionnaire and then employed five machine learning algorithms including random forest, decision trees, and support vector machine to analyze the Instagram post data of the participants. Finally, the models were utilized to determine the shopping orientations of the participants. The resulting accuracy rates of category prediction in the models ranged from 72.3% to 89.5%, which was fairly good. The results of this study can help businesses plan social media marketing and personalized product recommendations.
關鍵字(中) ★ 使用者輪廓
★ 購物傾向
★ Instagram
★ 使用者屬性建模
關鍵字(英) ★ User Profiling
★ Shopping Orientation
★ Instagram
★ User Attribute Modeling
論文目次 中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 viii
一、緒論 1
1-1  研究背景 1
1-2  研究動機 3
1-3  研究目的 4
二、文獻探討 6
2-1  使用者輪廓 (User Profiling) 6
2-1-1 性別 8
2-1-2 人格特質 10
2-2  購物傾向 12
2-2-1 市場行家 (Market Maven) 12
2-2-2 享樂購物 (Hedonic Shopping) 13
2-2-3 比較購物 (Comparison Shopping) 14
2-2-4 物質主義 (Materialism) 16
2-2-5 衝動購物 (Impulse Buying) 17

三、研究方法 20
3-1  研究設計與研究流程 20
3-1-1 資料蒐集 (Data Collection) 20
3-1-2 資料前處理 (Data Preprocessing) 21
3-1-3 資料標籤 (Data Labeling) 21
3-1-4 不平衡資料集處理 (Data Balancing) 22
3-1-5 模型訓練 (Modeling) 22
3-1-6 評估指標 (Evaluation) 22
3-2  屬性資料篩選與處理 24
3-2-1 貼文圖片 24
3-2-2 貼文特徵 29
四、實驗結果 33
4-1  資料描述 33
4-1-1 基本資料 33
4-1-2 信度與效度 35
4-1-3 資料平衡 37
4-1-4 屬性特徵分析 38
4-2  分類結果評估 39
4-2-1 貼文分類結果 40
4-2-2 使用者分類結果 42
五、結論與建議 45
5-1  研究發現 45
5-2  研究限制與未來展望 47
參考文獻 49
附錄一 購物傾向量表 56
參考文獻 Data Reportal. (Jan, 2021). DIGITAL 2021: GLOBAL OVERVIEW REPORT., Retrieved from: https://datareportal.com/reports/digital-2021-global-overview-report
Data Reportal. (Jan, 2021). DIGITAL 2021: TAIWAN., Retrieved from: https://datareportal.com/reports/digital-2021-global-overview-report
Google Cloud Vision API, Retrieved from: https://cloud.google.com/vision?hl=zh_tw
Ahtola, Olli T. (1985). “Hedonic and utilitarian aspects of consumer behavior: An attitudinal perspective”, ACR North American Advances , Vol. 21, pp.7-10.
Alowibdi, J. S, Buy, B. A and Yu, P. (2013). “Empirical evaluation of profile characteristics for gender classification on twitter.” 2013 12th International Conference on Machine Learning and Applications, IEEE.
Alowibdi, J. S, Buy, B. A and Yu, P. (2013). “Language independent gender classification on Twitter.”Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining.
Applebaum, W. (1951). “Studying customer behavior in retail stores”, Journal of marketing, Vol. 16, No. 2, pp.172-178.
Babin, B. J, Darden, W. R, and Griffin, M. (1994). “Work and/or fun: measuring hedonic and utilitarian shopping value”, Journal of consumer research, Vol. 20, No. 4, pp.644-656.
Barnes, S. J and Pressey, A. D. (2012). “In search of the “Meta‐Maven”: An examination of market maven behavior across real‐life, web, and virtual world marketing channels.” Psychology & Marketing, Vol. 29, No. 3, pp. 167-185.
Belk, R. W. (1984). “Three scales to measure constructs related to materialism: Reliability, validity, and relationships to measures of happiness”, Advances in Consumer Research, Vol. 11, Issue 1, pp. 291-297.
Bellenger, D. N, Robertson, D. H and Greenberg, B. A. (1977). “Shopping center patronage motives.” Journal of retailing, Vol. 53, No. 2, pp. 29-38.
Brancaleone, V. and Gountas, J. (2007). “Personality characteristics of market mavens”, North American - Advances in Consumer Research, Vol. 34.
Bussière, D. (2015). “Understanding the Market Maven: Personal and Social Characteristics.” In Proceedings International Marketing Trends Conference, pp. 1-18.
Chandon, P., Wansink, B. and Laurent, G. (2000). “A benefit congruency framework of sales promotion effectiveness”, Journal of marketing, Vol. 64, Issue 4, pp. 65-81.
Clover, V. T. (1950). “Relative importance of impulse-buying in retail stores”, Journal of marketing, Vol. 15, No. 1, pp. 66-70.
Darden, W. R, and Perreault Jr, W. D. (1976). “Identifying interurban shoppers: Multiproduct purchase patterns and segmentation profiles”, Journal of marketing research, Vol. 13, No. 1, pp. 51-60.
Darden, W. R and Reynolds, F. D. (1971). “Shopping orientations and product usage rates”, Journal of Marketing Research, Vol. 8, No. 4, pp. 505-508.
Darley, W. and Lim, J. S. (2018). “Mavenism and e-maven propensity: antecedents, mediators and transferability.” Journal of Research in Interactive Marketing, Vol. 12, No. 3, pp. 293-308.
Daun, Ake. (1983). “The materialistic life-style: Some socio-psychological aspects”, Consumer behavior and environmental quality, pp. 1-6.
Dodds, W. B, Monroe, K.B and Grewal, D. (1991). “Effects of price, brand, and store information on buyers product evaluations”, Journal of marketing research, Vol. 28, No. 3, pp. 307-319.
Duan, J. and Dholakia, R. R. (2018). “How purchase type influences consumption-related posting behavior on social media: The moderating role of materialism”, Journal of Internet Commerce, Vol. 17, Issue 1, pp. 64-80.
Feick, L. F, and Price, L. L. (1987). “The market maven: A diffuser of marketplace information”, Journal of marketing, Vol. 51, pp. 83-97.
Fernández, D., Moctezuma D. and Siordia, O. S. (2016). “Features combination for gender recognition on Twitter users.” 2016 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), pp. 1-6. IEEE.
Ferwerda, B., Schedl M. and Tkalcic, M. (2016). “Using instagram picture features to predict users’ personality.” International Conference on Multimedia Modeling, pp. 850-861. Springer.
Gao, R., Hao B., Bai S., Li L., Li A. and Zhu, T. (2013). “Improving user profile with personality traits predicted from social media content.” Proceedings of the 7th ACM conference on recommender systems, pp. 355-358.
Golbeck, J., Robles C., Edmondson M. and Turner, K. (2011). “Predicting personality from twitter.” In 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, pp. 149-156. IEEE.
Golbeck, J., Robles C. and Turner, K. 2011. “Predicting personality with social media.” CHI”11 extended abstracts on human factors in computing systems.
Guido, Gianluigi. (2006). “Shopping motives, big five factors, and the hedonic/utilitarian shopping value: An integration and factorial study”, Innovative Marketing, Vol. 2, pp. 57-67.
Han, K., Jo, Y., Jeon, Y., Kim, B., Song, J. and Kim, S. W. (2018). “Photos don’t have me, but how do you know me? Analyzing and predicting users on Instagram.” Adjunct publication of the 26th conference on user modeling, adaptation and personalization, pp. 251-256.
Han, K., Lee, S., Jang J. Y., Jung, Y. and Lee, D. (2016). “Teens are from mars, adults are from venus: analyzing and predicting age groups with behavioral characteristics in instagram.” Proceedings of the 8th ACM Conference on Web Science, pp. 35-44.
Harrigan, P., Daly, T.M, Coussement, K., Lee, J.A, Soutar, G.N and Evers, U. (2021). “Identifying influencers on social media”, International Journal of Information Management, Vol. 56, 102246.
Iyer, G. R, Blut, M., Xiao, S. H. and Grewal, D. (2020). “Impulse buying: a meta-analytic review”, Journal of the Academy of Marketing Science, Vol. 48, pp. 384-404.
Jeon, Y., Jeon, S. G. and Han, K. (2020). “Better targeting of consumers: Modeling multifactorial gender and biological sex from Instagram posts”, User Modeling and User-Adapted Interaction, Vol. 30, pp. 833-866.
Kesari, B. and Atulkar, S. (2016). “Satisfaction of mall shoppers: A study on perceived utilitarian and hedonic shopping values”, Journal of Retailing and Consumer services, Vol. 31, pp. 22-31.
Kubany, A., Ishay, S. B., Ohayon, R. S., Shmilovici, A., Rokach, L. and Doitshman, T. (2020). “Comparison of state-of-the-art deep learning APIs for image multi-label classification using semantic metrics”, Expert Systems with Applications, Vol. 161, Article 113656.
Lee, M. SW and Ahn, C. S. Y. (2016). “Anti‐consumption, materialism, and consumer well‐being”, Journal of Consumer Affairs, Vol. 50, Issue 1, pp. 18-47.
Li, H., Kuo, C. and Rusell, M. G. (1999). “The impact of perceived channel utilities, shopping orientations, and demographics on the consumer”s online buying behavior”, Journal of computer-mediated communication, Vol. 5, Issue 2, JCMC521.
Marquardt, J., Farnadi, G., Vasudevan, G., Moens, M.F., Davalos, S., Teredesai, A. and Martine D. C. (2014). “Age and gender identification in social media”, Proceedings of CLEF 2014 Evaluation Labs, 1180, pp. 1129-1136.
Ohanian, R., and Tashchian A. (1992). “Consumers shopping effort and evaluation of store image attributes: the roles of purchasing involvement and recreational shopping interest”, Journal of Applied Business Research (JABR), Vol. 8, pp. 40-49.
Olsen, S. O., Tudoran, A. A., Honkanen, P. and Verplanken, B. (2016). “Differences and similarities between impulse buying and variety seeking: A personality‐based perspective”, Psychology & Marketing, Vol. 33, Issue 1, pp. 36-47.
Peersman, C., Daelemans, W. and Van Vaerenbergh, L. (2011). “Predicting age and gender in online social networks.” Proceedings of the 3rd international workshop on Search and mining user-generated contents, pp. 37-44.
Pennacchiotti, M. and Popescu, A. M. (2011). “A machine learning approach to twitter user classification.” International Conference on Weblogs and Social Media.
Rao, D., Yarowsky, D., Shreevats, A. and Gupta, M. (2010). “Classifying latent user attributes in twitter.” Proceedings of the 2nd international workshop on Search and mining user-generated contents, pp. 37-44.
Rassuli, K. M, and Hollander, S. C. (1986). “Desire-induced, innate, insatiable?”, Journal of Macromarketing, Vol. 6, pp. 4-24.
Reynaldo, N, Chanrico, W., Suhartono, D. and Purnomo, F. (2019). “Gender Demography Classification on Instagram based on User”s Comments Section”, Procedia Computer Science, Vol. 157, pp. 64-71.
Reynolds, K. E and Beatty, S. E. (1999). “Customer benefits and company consequences of customer-salesperson relationships in retailing”, Journal of retailing, Vol. 75, Issue 1, pp. 11-32.
Richins, M. L and Dawson, S. (1992). “A consumer values orientation for materialism and its measurement: Scale development and validation”, Journal of consumer research, Vol. 19, pp. 303-316.
Rook, Dennis W. (1987). “The buying impulse”, Journal of consumer research, Vol. 14, Issue 1, pp. 189-199.
Schultz, L. and Adams, M. (2018). “Evaluation of Google Vision API for Object Detection in General Subject Images.” Proceedings of the Conference on Information Systems Applied Research.
Schwartz, H A., Eichstaedt, J. C , Kern, M. L, Dziurzynski, L., Ramones, S. M, Agrawal, M., Shah, A., Kosinski, M., Stillwell, D. and Seligman, M. EP. (2013). “Personality, gender, and age in the language of social media: The open-vocabulary approach”, PloS one, Vol. 8.
Singh, J., Wheeler, J., Fong, N. and Chaudhary, S. “A Comparison of Public Cloud Computer Vision Services”.
Stern, Hawkins. (1962). “The significance of impulse buying today”, Journal of marketing, Vol. 26, No. 2, pp. 59-62.
Stone, Gregory P. (1954). “City shoppers and urban identification: observations on the social psychology of city life”, American Journal of Sociology, Vol. 60, No. 1, pp. 36-45.
Verplanken, B. and Herabadi, A. (2001). “Individual differences in impulse buying tendency: Feeling and no thinking”, European Journal of personality, Vol. 15, S71-S83.
Vicente, M., Batista, F. and Carvalho, J. P. (2019). “Gender detection of Twitter users based on multiple information sources.” Interactions Between Computational Intelligence and Mathematics Part 2 (Springer), pp. 39-54.
Vicente, M., Batista, F. and Carvalho, J. P. (2015). “Twitter gender classification using user unstructured information.” In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-7. IEEE.
Wang, L., Li, Q., Chen, X. and Li, S. (2016). “Multi-task learning for gender and age prediction on chinese microblog.” Natural Language Understanding and Intelligent Applications (Springer).
Workman, J. E, Lee, S. H. and Liang, Y. (2020). “Social Media Engagement, Gender, Materialism, and Money Attitudes.” In International Textile and Apparel Association Annual Conference Proceedings, Vol. 77, No. 1, Iowa State University Digital Press.
Yang, Hongwei. (2013). “Market mavens in social media: Examining young Chinese consumers’ viral marketing attitude, eWOM motive, and behavior”, Journal of Asia-Pacific Business, Vol. 14, Issue 2, pp. 154-178.
You, Q., Bhatia, S., Sun, T. and Luo, J. (2014). “The eyes of the beholder: Gender prediction using images posted in online social networks.” 2014 IEEE International Conference on Data Mining Workshop, pp. 1026-1030. IEEE.
Yu, C. and Bastin, M. (2017). “Hedonic shopping value and impulse buying behavior in transitional economies: A symbiosis in the Mainland China marketplace.” Advances in Chinese Brand Management (Springer), pp. 316-330.
王培倫,「星座對於消費者在購物傾向上之影響-以大台北地區大學生為例」,國立政治大學,碩士論文,民國92年。
指導教授 許文錦(Wen-Chin Hsu) 審核日期 2021-7-5
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明