Application of Text Mining on the Editorial of a Newspaper of Bangladesh

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2019
Tarequl Islam Manir, Md. Moyazzem Hossain

Tarequl Islam Manir and Md. Moyazzem Hossain. Application of Text Mining on the Editorial of a Newspaper of Bangladesh. International Journal of Computer Applications 178(11):23-29, May 2019. BibTeX

	author = {Tarequl Islam Manir and Md. Moyazzem Hossain},
	title = {Application of Text Mining on the Editorial of a Newspaper of Bangladesh},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2019},
	volume = {178},
	number = {11},
	month = {May},
	year = {2019},
	issn = {0975-8887},
	pages = {23-29},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2019918837},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The development in the fields of web, digital libraries, technical documentation, and medical data has made it easier to access a larger amount of a textual document. Owing to the increasing demands to obtain knowledge from a large number of textual documents accessible on the web, text mining is gaining significant importance. The aim of this paper is to find the topmost ten frequent words of the whole writing of the Editorial writing appeared in the most popular Bangladeshi Daily English newspaper entitled “The Daily Star” over the period 01 January 2018 to 30 June 2018. Finally, we representation of text data visually with the help of word cloud. The results indicate that the most frequent word highlighted is ‘government’ in the writing of all months considered in this study. Also, the words “Bangladesh”, “Myanmar”, “people”, “must” and “will” emerge in the analysis.


  1. Sagayam, R. 2012. A survey of text mining: Retrieval, extraction and indexing techniques. International Journal of Computational Engineering Research. 2(5), 1443-1446.
  2. Padhy, N., Mishra, D. and Panigrahi, R. 2012. The survey of data mining applications and feature scope. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT). 2(3), 43-58. Doi: 10.5121/ijcseit.2012.2303.
  3. Fan, W., Wallace, L., Rich, S. and Zhang, Z. 2006. Tapping the power of text mining. Communications of the ACM. 49(9), 76–82. Doi: 10.1145/1151030.1151032.
  4. Liao, S. H., Chu, P. H. and Hsiao, P. Y. 2012. Data mining techniques and applications–a decade review from 2000 to 2011. Expert Systems with Applications. 39(12), 11303–11311. Doi: 10.1016/j.eswa.2012.02.063.
  5. He, W. 2013. Examining students online interaction in a live video streaming environment using data mining and text mining. Computers in Human Behavior. 29(1), 90–102. Doi: 10.1016/j.chb.2012.07.020.
  6. Jusoh, S. and Alfawareh, H. M. 2012. Techniques, Applications and Challenging Issue in Text Mining. IJCSI International Journal of Computer Science Issues. 9(6), 431-436.
  7. Nightingal, J., 2006. Digging for data that can change our world. The Guardian, 10 January, 2006. Accessed on 12 February 2018. Available at
  8. Feldman, R. and Dagan, I. 1995. Knowledge Discovery in Textual Databases
  9. (KDT). KDD'95 Proceedings of the First International Conference on Knowledge Discovery and Data Mining. 112–117.
  10. Jusoh, S. and Alfawareh, H. M. 2007. Natural language interface for online sales. Proceedings of the International Conference on Intelligent and Advanced System (ICIAS2007). Malaysia: IEEE, 224–228.
  11. Rao, R. 2003. From unstructured data to actionable intelligence. IT Professional. 5(6), 29-35. Doi: 10.1109/MITP.2003.1254966.
  12. Karanikas, H., Tjortjis, C. and Theodoulidis, B. 2000. An approach to text mining using information extraction. Proceedings of Workshop of Knowledge Management: Theory and Applications. September 13-16, 2000, Lyon, France.
  13. Hale, R. 2005. Text mining: Getting more value from literature resources. Drug Discovery Today. 10(6), 377–379.
  14. Chakrabarti, S., 2000. Mining the Web: Analysis of Hypertext and Semi Structured Data. San Francisco, CA: Morgan Kaufman.
  15. Roul R.K., Varshneya S., Kalra A., Sahay S.K. 2015. A Novel Modified Apriori Approach for Web Document Clustering. In: Jain L., Behera H., Mandal J., Mohapatra D. (eds) Computational Intelligence in Data Mining - Volume 3. Smart Innovation, Systems and Technologies, vol 33. Springer, New Delhi. Doi: 10.1007/978-81-322-2202-6_14.
  16. Sumathy, K. and Chidambaram, M. 2013. Text mining: Concepts, applications, tools and issues-an overview. International Journal of Computer Applications. 80(4), 29-32. Doi: 10.5120/13851-1685.
  17. Patel, M. R. and Sharma, M. G. 2014. A survey on text mining techniques. International Journal of Engineering and Computer Science. 3(5), 5621-5625.
  18. Jhanji, D. and Garg, P. 2014. Text Mining. International Journal of Scientific Research and Education. 2(8), 1642-1648.
  19. Kaushik, A. and Naithani, S. 2016. A Comprehensive Study of Text Mining Approach. International Journal of Computer Science and Network Security. 16(2), 69-76.
  20. Nie, B. and Sun, S. 2017. Using Text Mining Techniques to Identify Research
  21. Trends: A Case Study of Design Research. Applied Sciences. 7, 401; Doi: 10.3390/app7040401.
  22. Laxman, B. and Sujatha, D. 2013. Improved method for pattern discovery in text mining. International Journal of Research in Engineering and Technology. 2(1), 2321–2328.
  23. Dang, D. S. and Ahmad, P. H. 2015. A review of text mining techniques associated with various application areas. International Journal of Science and Research (IJSR). 4(2), 2461–2466.
  24. Steinberger, R. 2012. A survey of methods to ease the development of highly multilingual text mining applications. Language Resources and Evaluation. 46(2), 155–176. Doi: 10.1007/s10579-011-9165-9.
  25. Zhong, N., Li, Y. and Wu, S. T. 2012. Effective pattern discovery for text mining. IEEE transactions on knowledge and data engineering. 24(1), 30–44. Doi: 10.1109/TKDE.2010.211.
  26. Mukhedkar, B. A., Sakhare, D. and Kumar, R. 2016. Pragmatic analysis based document summarization. International Journal of Computer Science and Information Security. 14(4), 145-149.
  27. Chen, C. P. and Zhang, C. Y. 2014. Data-intensive applications, challenges, techniques and technologies: A survey on big data. Information Sciences, 275, 314–347. Doi: 10.1016/j.ins.2014.01.015.
  28. Al-Hashemi, R. 2010. Text summarization extraction system (tses) using extracted keywords. International Arab Journal of e-Technology. 1(4), 164– 168.
  29. Jayashankar, S. and Sridaran, R. 2016. Superlative model using word cloud for short answers evaluation in e-Learning. Education and Information Technologies. 22(5), 2383-2402. Doi: 10.1007/s10639-016-9547-0.
  30. DePaolo, C. A. and Wilkinson, K., 2014. Get your head into the clouds: using word clouds for analyzing qualitative assessment data. Tech Trends. 58(3), 38–44. Doi: 10.1007/s11528-014-0750-9.
  31. Sinclair, J., and Cardew-Hall, M. 2008. The folksonomy tag cloud: when is it useful?. Journal of Information Science. 34(1), 15–29. Doi: 10.1177/0165551506078083.
  32. Viegas, F. B., Wattenberg, M., Van Ham, F., Kriss, J. and McKeon, M. 2007. Many eyes: a site for visualization at internet scale. IEEE Trans. Vis. Computer Graphics. 13(6), 1121–1128.


Text Mining; Editorial; Frequent Words; Information Extraction; Word Cloud.