شناسایی و اولویت‌بندی کاربردهای شبکه کاوی در تجارت الکترونیکی

نوع مقاله: مقاله پژوهشی

نویسندگان

1  استادیار، گروه مدیریت فناوری اطلاعات دانشکده مدیریت دانشگاه تهران، تهران. (نویسنده مسئول

2 کارشناس ارشد، مدیریت فناوری اطلاعات، دانشگاه تهران،

3 استادیار، گروه مدیریت فناوری اطلاعات دانشکده مدیریت دانشگاه تهران، ،

چکیده

امروزه اطلاعات موجود در شبکه‌های اجتماعی از ارزش بسیاری برخوردارند و مبنای توسعه‌ی کسب‌و‌کارها هستند. هدف این تحقیق ارائه‌ی چارچوبی است که بتواند کاربردهای حاصل از تحلیل شبکه‌های اجتماعی را در تجارت الکترونیک ارائه کند. در این پژوهش ابتدا در بخش اول کاربردهای شبکه کاوی با استفاده از روش تحلیل محتوا در ابعاد مختلف PEST ) سیاسی، اجتماعی، اقتصادی و فنی (استخراج شده و سپس در بخش دوم کاربردهای یافت شده در دو بخش فنی و اقتصادی انتخاب و با استفاده از پیمایش نظر خبرگان و آزمون‌های آماری کاربردهای با اهمیت‌تر از میان این کاربردها شناسایی شدند. در گام بعد جهت اولویت‌بندی کاربردها با استفاده از روش تحلیل سلسله مراتبی(AHP)، مقایسات زوجی صورت گرفته و با توزیع پرسشنامه‌ی مقایسات زوجی میان خبرگان داده‌های موردنظر جمع‌آوری و سپس کاربردها به ترتیب اهمیت اولویت‌بندی شدند. نتایج نشان داد در بعد اقتصادی کشف کلاه‌برداری، کشف نیازها و علایق مشتریان و در بعد فنی بهبود وب‌سایت‌های تجارت الکترونیکی و شناسایی ترافیک شبکه بالاترین جایگاه را دارند.v

کلیدواژه‌ها


عنوان مقاله [English]

Identification and Ranking of Social Mining Applications in E-Commerce

نویسندگان [English]

  • Saeid Rohan 1
  • Samaneh Amirian 2
  • Ayoub Mohammadian 3
1 Assistant Professor, Department of Information Technology, Faculty of Management, University of Tehran, Tehran.(Corresponding Author: SRouhani@ut.ac.ir)
2 MA, Information Technology Management, University of Tehran, Tehran, Iran
3 Assistant Professor, Department of Information Technology, Faculty of Management, University of Tehran, Tehran
چکیده [English]

In recent years, we have observed the rapid development of social media, which has drastically transformed the way in which people communicate and obtain information. Nowadays, customers on e-commerce sites mostly rely on comments posted by customers, producers, and service providers. In this research, in the first part the application of network mining is extracted using content analysis method in different dimensions of Political, Social, Economic and Technical (PEST). Then, in the second part, the applications used in two technical and economic sections are selected and using surveying expert opinion and statistical tests the more important applications have been identified among these applications. In the next step, to prioritize the applications using Analytic Hierarchy Process (AHP), paired comparisons were performed and by distributing pairwise comparison questionnaire data was collected and then the applications were ranked in order of priority. The results showed that in the economic dimension, the discovery of fraud, the discovery of the needs and interests of customers, and in the technical dimention improving e-commerce and identifying network traffic have the highest rank

کلیدواژه‌ها [English]

  • Applications
  • Social Mining
  • E-commerce
  • Identification and Ranking
 

افتاده، جواد، (1394). تحلیل شبکه های اجتماعی، انتشارات ثانیه،

هژبر، ابراهیم (1393)، داده کاوی، مفاهیم و کاربردها. دانشگاه آزاد اسلامی، ورزقان.

 

Biggs,N.,Lioyd,E. and Wilson,R.(1986).Graph Theory. Oxford: Oxford University Press.

Bindu,P., Thilagam, P. (2016). “Mining social networks for anomalies: Methods and challenges”. Department of Computer Science and Engineering. Surathka: National Institute of Technology Karnataka.

Bonchi, F., CASTILLO, C. and GIONIS, A. (2011). “Social Network Analysis and Mining for Business Applications”. ACM Trans. Intell. Syst. Technol. 2, 3, Article 22 (April 2011), 37 pages.

Borgatti, S.P. and halgin, D.S. (2010).”Analyzing Affiliation Networks”. Gatton College Of Business and Economics. Lexington, KY: University of Kentucky.

Callado, A., Judith Kelner, A. and  Sadok, D. (2010). “Better network traffic identification through the independent combination of techniques”. Quixada : Federal University of Ceara.

Camacho, D. (2015). “Social Big Data: Recent achievements and new challenges”. Computer Science Department. Spain: Universidad Autónoma de Madrid.

Carmona, C., Ramírez-Gallego,S. (2012). “Web usage mining to improve the design of an e-commerce website: OrOliveSur.com”. Department of Computer Science. Jaén: University of Jaén.

Chen, J., Chen, H, wu, Z. and Hu, D. (2016). “Forecasting smog-related health hazard based on social media and physical sensor”. College of Computer Science. Hangzhou: Zhejiang University.

Chen, L., Qi , L. and Wang, F. (2012). “omparison of feature-level learning methods for mining online consumer reviews”. Department of Computer Science. Hong Kong: Hong Kong Baptist University.

Chen, X. (2014). “Mining Social Media Data for Understanding Students’ Learning Experiences”. School of Engineering Education. West Lafayette: Purdue University Lafayette.

S. Dumais, T. Joachims, K. Bharat, A. Weigend, Sigir (2003) workshop report: “implicit measures of user interests and preferences”, ACM SIGIR Forum (Fall) (2003).

Goswami, S., Chakraborty, S. and Ghosh, S. (2016). “A review on application of data mining techniques to combat natural disasters”. Institute of Engineering and Management. Kolkata : A.K.Choudhury School of Information Technology.

Haibin Liu, Vlado Kesˇelj,(2006). “Combined mining of Web server logs and web contents for classifying user navigation patterns and predicting users’ future requests”. Data & Knowledge Engineering 61 (2007) 304–330

Hansen, D.L, SHnederman, B. and Smith,M.A. (2011). “analyzing social media networks with NodeXL”

Hu, c. and Racherla, P. (2008).”visual representation of knowledge networks: a socal network analysis of hospitality research domain ”. International Journal of Hospitality Management,27 (2): 302-312.

Jahanbakhsh, K., Moon, Y. (2014). “The Predictive Power of Social Media: On the Predictability of U.S. Presidential Elections using Twitter”. Sociology Department. Seoul: Yonsei University.

Jang, H., Sim, J. and Lee,Y. (2013). “Deep sentiment analysis: Mining the causality between personality-value-attitude for analyzing business ads in social media”. Global Science Data Center (GSDC). Korea: Institute of Science Technology Information.

Kautz, H., (2013). “Data Mining Social Media for Public Health Applications”. Rochester: University of Rochester.

Lipizzi, C., Iandoli, L. and Marquez, J. (2015). “Extracting and evaluating conversational patterns in social media: A socio-semantic analysis of customers’ reactions to the launch of new products using Twitter streams”. School of Systems and Enterprise. USA: Stevens Institute of Technology.

Maher, C. and Ryan, J. (2015). “Social media and applications to health behavior”. School of Health Sciences, Sansom Institute. Adelaide: University of South Australia.

Naaman, M., Schwartz, R. and Teodoro, R. (2015). “Editorial Algorithms: Using Social Media to Discover and Report Local News”. School of Communication and Information. Rutgers University.

Nguyen, T., Shirai, K. and Velcin, J. (2015). “Sentiment analysis on social media for stock movement prediction”. School of Information Science. Japan: Advanced Institute of Science and Technology.

 Nohuddin, P. (2012). “Predictive Trend Mining for Social Network Analysis”. the University of Liverpool for the degree of Doctor in Philosophy.

Ravindran, S. (2015). “Mastering Social Media Mining with R”. BIRMINGHAM – MUMBAI.

Romero, C., Ventura, S. and Zafra, A. (2009). “Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems”. Department of Computer Sciences and Numerical Analysis. Córdoba: University of Córdoba.

Saaty, T.L. and L. T. Tran, (2007) On the invalidity of fuzzifying numerical judgments in the Analytic Hierarchy Process, Mathematical and Computer Modelling.

Scott, John (1991). Social Network Analysis. London: sage.

Shi, L., Agarwal, N. and Agrawal, A. (2012). “Predicting US Primary Elections with Twitter”. R&D, Opera Solutions.

Su, J., Chang, W. and Tseng, V. (2013). “Personalized Music Recommendation by Mining Social Media Tags”. Department of Information Management. Taoyuan: Kainan University.

Sun, J., Wang, G. and Cheng, X. (2014). “Mining affective text to improve social media item recommendation”. School of Management. Hefei: Hefei University of Technology.

Tanbeer, S., Leung, C. and Cameron, J. (2014). “Interactive Mining of Strong Friends from Social Networks and Its Applications in E-Commerce”. Journal of Organizational Computing and Electronic Commerce.

Thiel, K., Kotter, T. and Berthold, M. (2012). “Creating Usable Customer Intelligence from Social Media Data: Network Analytics meets Text Mining”.

Thelwall, M. (2016). “TensiStrength: Stress and relaxation magnitude detection for social media texts”. School of Mathematics and Computer Science. Wolverhampton: University of Wolverhampton.

Tseng, M. (2016). “Using social media and qualitative and quantitative information scales to benchmark corporate sustainability”. Department of Business Administration. Kweishan: Lunghwa University of Science and Technology.

Tsvetovat, M., and Kouznetsov, A. (2011). Social Network analysis For Startups. O’ Reilly Media.

Wasserman, Stanley and Katherine Faust (1994). Social Network analysis: Methods and Applications. Cambridge: Cambridge University Press.

Wellman, Barry and S.D. Berkowitz, Eds (1988). Social Structures: A Network Approach. Cambridge: Cambridge University Press.

Yakushev, A. and Mityagin, S. (2014). Social networks mining for analysis and modeling drugs usage. Paper presented at the 14th International Conference on Computational Science.

Yuan, Y., Liu, Y. and Wei, G. (2016). “Exploring inter-country connection in mass media: A case study of China”. Department of Geography. San Marcos: Texas State University.