Text Mining Methodology for Effective Online Marketing

Authors

  • Sadik Khan  Assistant Professor, Department of Computer Science & Engineering, Institute of Engineering & Technology, Bundelkhand University, Jhansi, India

DOI:

https://doi.org//10.32628/CSEIT12283129

Keywords:

Text Mining, Online Marketing, Data Analysis, Customer Reviews, Social Media, Sentiment Analysis, Data Preprocessing, Marketing Decision-Making.

Abstract

Ongoing efforts are made by businesses to optimize their digital marketing strategies and reach their intended audience. Text mining, as part of data mining, uncovers data analysis insights thanks to its textual data focus. Focused on the exploration of marketing applications, this study paper delves into the benefits of text mining in marketing strategies. Data analysis of customer reviews, social media posts, and other text-based sources can help businesses understand customer preferences, sentiment, and behavior. Text mining stages are detailed, covering data collection, preprocessing, analysis, and interpretation, in this paper. Through text mining, online marketing can be put into practice with relevant examples and case studies. Text mining has a profound impact on marketing decision-making, according to this research, and provides valuable insights for businesses to harness this power successfully.

References

  1. S. Amarakoon and A. Caldera, "Text mining: Finding right documents from large collection of unstructured documents," The 3rd International Conference on Data Mining and Intelligent Information Technology Applications, Macao, China, 2011, pp. 5-10.
  2. N. Zhong, Y. Li and S. -T. Wu, "Effective Pattern Discovery for Text Mining," in IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 1, pp. 30-44, Jan. 2012, doi: 10.1109/TKDE.2010.211.
  3. T. Matsumoto, W. Sunayama, Y. Hatanaka and K. Ogohara, "Data Analysis Support by Combining Data Mining and Text Mining," 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), Hamamatsu, Japan, 2017, pp. 313-318, doi: 10.1109/IIAI-AAI.2017.165.
  4. Y. Zhang, M. Chen and L. Liu, "A review on text mining," 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China, 2015, pp. 681-685, doi: 10.1109/ICSESS.2015.7339149.
  5. D. Agnihotri, K. Verma and P. Tripathi, "Pattern and Cluster Mining on Text Data," 2014 Fourth International Conference on Communication Systems and Network Technologies, Bhopal, India, 2014, pp. 428-432, doi: 10.1109/CSNT.2014.92.
  6. N. Vedula et al., "Multimodal Content Analysis for Effective Advertisements on YouTube," 2017 IEEE International Conference on Data Mining (ICDM), New Orleans, LA, USA, 2017, pp. 1123-1128, doi: 10.1109/ICDM.2017.149.
  7. C.Faloutsos. Access Methods for Text. ACM Comput. Surv. , 17 p49-74, 1985.
  8. G.Salton. Automatic Text Processing. Reading, MA: Addison-Wesley,1989.
  9. C.J.Van Rijsbergen. Information Retrieval. Butterworth,1990.
  10. C.T.Yu and W.Meng. Principles of Database Query Processing for Advanced Applications. San Francisco: Morgan Kaufmann,1997.
  11. K.Wang,S.Zhou,and S.C.liew. Building HierarchicalClassifiers Using Class Proximity. In Proc.1999 Int. Conf. VLDB’99,P363-374,Edinburgh,UK,Sept.1999.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-11-30

Issue

Volume 3, Issue 8, November-December-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Sadik Khan, " Text Mining Methodology for Effective Online Marketing, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 8, pp.465-469, November-December-2018. Available at doi : https://doi.org/10.32628/CSEIT12283129