Business Intelligence Management Studies

The Position of Books and Reading on the Site of Tebyan Based on Text Mining and Analysis of Social networks

Authors

Salma Keshavarzian 1
Hoorieh Barardokht 2

1 MA, Knowledge and Information Science, Shahid Beheshti University., (Corresponding Author: salmak1351@gmail.com

2 Graduate Student, Knowledge and Information Science, Shiraz University, Shiraz

https://doi.org/10.22054/ims.2018.8516
Abstract
 
In this study, focusing on the subject of the study, the site of Tebyan Institute of Cultural and Information Science has been carefully analyzed using subject clustering. Using clustering and text mining, while identifying outstanding and insignificant clusters, the importance of this issue among the users of Tebyan network has been discussed. The results indicate that due to the cultural and information nature of Tebyan social network, searching the reading and study keywords will provide most relevant results in thematic category of culture with 26% and 18%, respectively. Due to the virtual and electronic nature of Tobayan network, it is necessary that the electronic education section of this site cover the shortage by generating more educational contents. Despite the high number of results in the thematic category of society, the results in the whole network seems to be very low for reading (only 952 and 1199, respectively, in the search for key terms of reading and study), and this requires more planning for promoting the importance and value of reading issues in the society by Tebyan. The international community brings modern technology to the society in the light of new developments and in the meanwhile, if anyone is left behind in the knowledge of the day, he has no opportunity to compensate for the lost resources; therefore, (s)he should be deeply thought-out and broadly planned for the importance and value of people to read, study and promote, even on social networks.
 

Keywords

Big Data Analysis Clustering
Reading
Search Query Data
Text Mining
 
اسماعیلی، مهدی.، زاهد، عطیه. (1394). مروری بر متن کاوی؛ تکنیکها و چالشها، سومین کنفرانس بین المللی پژوهشهای کاربردی در مهندسی کامپیوتر و فناوری اطلاعات.
پیکری، ناصر.، یعقوبی، سیدعلی اصغر.، طاهری، حمیدرضا. (1394). تحلیل احساسات در شبکه اجتماعی توئیتر با تکنیک متن کاوی، کنفرانس بین المللی وب پژوهی.
عظیمی همت. منیره.، و فاطمه. شمس عزت، (۱۳۹۴). مروری بر متن کاوی متون فارسی، دومین کنفرانس بین المللی و سومین همایش ملی کاربرد فناوری های نوین در علوم مهندسی، مشهد، دانشگاه تربت حیدریه، دانشگاه فردوسی مشهد.
کرمی، مهتاب. (1386). کاربرد ابزارهای تحلیلگر داده کاوی و متن کاوی در چابکی سازمانهای مراقب بهداشتی و درمانی، فصلنامه علمی-پژوهشی مدیریت سلامت، 10 (30)، 15-21.
Sultan M. Al-Daihani, Alan Abrahams. (2016). A Text Mining Analysis of Academic Libraries' Tweets, the Journal of Academic Librarianship.
G. Bello-Orgaz, J. Hernandez-Castro, D. Camacho, (2016). Detecting discussion communities on vaccination in twitter, Future Generation Computer Systems.
Brynjolfsson, E., Geva, T., & Reichman, S. (2016). Crowd-squared: Amplifying the predictive power of search trend data. MIS Quarterly, 40(3).
Shraddha S.Bhanuse, Shailesh D.Kamble, Sandeep M. Kakde. (2016). Text Mining using Metadata for Generation of Side information, Procedia Computer Science, 78, 807 – 814.
Feldman, R., and Sanger, J. (2006). The Text Mining Handbook. New York: Cambridge University Press. ISBN 978-0-521-83657-9.
Marijn Janssen, Haiko van der Voort, Agung Wahyudi. (2017). Factors influencing big data decision-making quality, Journal of Business Research, Volume 70, January 2017, Pages 338–345.
Jaeyong Kang, Hyunju Lee, (2017). Modeling user interest in social media using news media and Wikipedia, Information Systems, Volume 65, April 2017, Pages 52–64.
Kyung-Sun Kim, Sei-Ching Joanna Sin. (2016). Use and Evaluation of Information from Social Media in the Academic Context: Analysis of Gap between Students and Librarians, the Journal of Academic Librarianship, 42, 74–82.
Allison J. Lazard, Emily Scheinfeld, Jay M. Bernhardt, Gary B. Wilcox, Melissa Suran. (2015). detecting themes of public concern: A text mining analysis of the Centers for Disease Control and Prevention’s Ebola live Twitter chat, American Journal of Infection Control,1-3.
J. Han, H. Lee. (2014). Characterizing user interest using heterogeneous media, in: Proceedings of the 23nd international conference on World Wide Web, pp. 289-290.
Xin Li, Bing Pan, Rob Law, Xiankai Huang, (2017). Forecasting tourism demand with composite search index, Tourism Management, 59, 57-66.
Juyoung Song, Tae Min Song, Dong-Chul Seo, Jae HyunJin. (2016). Data Mining of Web-Based Documents on Social Networking Sites That Included Suicide-Related Words among Korean Adolescents, Journal of Adolescent Health, Volume 59, Issue 6, Pages 668-673.
MohammadNoor Injadat, Fadi Salo, Ali Bou Nassif. (2016). Data mining techniques in social media: A survey, Neurocomputing, Volume 214, Pages 654-670.
 
 
 
 

 

Business Intelligence Management Studies
Volume 6, Issue 21
Autumn 2017
Pages 169-188

  • Receive Date 30 May 2018

Editorial Boards

Submit Manuscript

Article Processing Charges

Contact Us