Classification of Unwanted SMS Data (Spam) with Text Mining Techniques

Year 2022, Volume: 3 Issue: 2, 41 - 50, 28.12.2022

Rasim Çekik

https://doi.org/10.55195/jscai.1210559

Abstract

Text mining, which derives information from written sources such as websites, books, e-mails, articles, and online news, processes and structures data using advanced approaches. The vast majority of SMS (Short Message Service) messages are unwanted short text documents. Effectively classifying these documents will aid in the detection of spam. The study attempted to identify the most effective techniques on SMS data at each stage of text mining. Four of the most well-known feature selection approaches were used, each of which is one of these parameters. As a result, the strategy that yielded the best results was chosen. In addition, another parameter that produces the best results with this approach, the classifier, was determined. The DFS feature selection approach produced the best results with the SVM classifier, according to the experimental results. This study establishes a general framework for future research in this area that will employ text mining techniques.

Keywords

Text Mining, Text Classification, Spam SMS, Feature Selection

References

• Parlak, B., & Uysal, A. K. (2021). A novel filter feature selection method for text classification: Extensive Feature Selector. Journal of Information Science, 0165551521991037.
• Uysal, A. K., & Gunal, S. (2012). A novel probabilistic feature selection method for text classification. Knowledge-Based Systems, 36, 226-235.
• Android Apps. (Accessed March 2012). Available: https://play.google.com/store/apps
• Delany, S. J., Buckley, M., & Greene, D. (2012). SMS spam filtering: Methods and data. Expert Systems with Applications, 39(10), 9899-9908.
• Xiang, Y., Chowdhury, M., & Ali, S. (2004). Filtering mobile spam by support vector machine. In N. Debnath (Ed.), Proceedings of the third international conference on computer sciences, software engineering, information technology, E-business and applications (pp. 1–4).
• Abayomi-Alli, O., Misra, S., Abayomi-Alli, A., & Odusami, M. (2019). A review of soft techniques for SMS spam classification: Methods, approaches and applications. Engineering Applications of Artificial Intelligence, 86, 197-212.
• Nagwani, N. K., & Sharaff, A. (2017). SMS spam filtering and thread identification using bi-level text classification and clustering techniques. Journal of Information Science, 43(1), 75-87.
• Nagwani, N. K. (2017). A Bi-Level Text Classification Approach for SMS Spam Filtering and Identifying Priority Messages. International Arab Journal of Information Technology (IAJIT), 14(4).
• Hanif, K., & Ghous, H. Detectıon Of Sms Spam And Fılterıng By Usıng Data Mınıng Methods: Lıterature Revıew.
• Gupta, M., Bakliwal, A., Agarwal, S., & Mehndiratta, P. (2018). A Comparative Study of Spam SMS Detection Using Machine Learning Classifiers. 2018 11th Internationalfile:///E:/Sms Spamming/Sms Spamming 15.Pdf Conference on Contemporary Computing, IC3 2018, 1–7. https://doi.org/10.1109/IC3.2018.8530469
• Popovac, M., Karanovic, M., Sladojevic, S., Arsenovic, M., & Anderla, A. (2018). Convolutional Neural Network Based SMS Spam Detection. 2018 26th Telecommunications Forum, TELFOR 2018 - Proceedings, 1–4. https://doi.org/10.1109/TELFOR.2018.8611916
• Lu Zhang, Jiandong Ding, Yi Xu, Yingyao Liu, and Shuigeng Zhou. 2021. Weakly-supervised text classification based on keyword graph. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2803–2813, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
• Uysal, A. K., Günal, S., Ergin, S., & Günal, E. Ş. (2012, April). Detection of SMS spam messages on mobile phones. In 2012 20th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). Ieee.
• Abayomi-Alli, O., Misra, S., Abayomi-Alli, A., & Odusami, M. (2019). A review of soft techniques for SMS spam classification: Methods, approaches and applications. Engineering Applications of Artificial Intelligence, 86, 197-212.”
• Xiang, Y., Chowdhury, M., & Ali, S. (2004). Filtering mobile spam by support vector machine. In N. Debnath (Ed.), Proceedings of the third international conference on computer sciences, software engineering, information technology, E-business and applications (pp. 1–4).
• Boykin, P. O., & Roychowdhury, V. P. (2005). Leveraging social networks to fight spam. IEEE Computer, 38, 61–68.
• Rao, S., Verma, A. K., & Bhatia, T. (2021). A review on social spam detection: challenges, open issues, and future directions. Expert Systems with Applications, 186, 115742.
• Healy, M., Delany, S., & Zamolotskikh, A. (2005). An assessment of case-based reasoning for short text message classification. In N. Creaney (Ed.), Proceedings of 16th Irish conference on artificial intelligence and cognitive science, (AICS-05) (pp. 257–266).
• Gómez Hidalgo, J. M., Bringas, G. C., Sánz, E. P., & Garcı ´a, F. C. (2006). Content based SMS spam filtering. In D. Bulterman, & D.F. Brailsford (Eds.), Proceedings of the 2006 ACM symposium on document engineering DocEng ’06 (pp. 107–114). New York, NY, USA: ACM.
• Cai, J., Tang, Y., & Hu, R. (2008). Spam filter for short messages using winnow. In Proceedings of the international conference on advanced language processing and web information technology (pp. 454–459). IEEE.
• Wu, N., Wu, M., & Chen, S. (2008). Real-time monitoring and filtering system for mobile SMS. In Proceedings of 3rd IEEE conference on industrial electronics and applications (pp. 1319–1324).
• Jie, H., Bei, H., & Wenjing, P. (2010). A Bayesian approach for text filter on 3G network. In Proceedings of the 6th international conference on wireless communications networking and mobile computing (pp. 1–5).
• Longzhen, D., An, L., & Longjun, H. (2009). A new spam short message classification. In Proceedings of the first international workshop on education technology and computer science (Vol. 2, pp. 168 –171).
• Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. In European conference on machine learning (s. (pp. 137-142). ). Berlin, Heidelberg.: Springer, .
• Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., & Brown, D. (2019). Text classification algorithms: A survey. Information.
• Pearson, E. (1925). Bayes’ theorem, examined in the light of experimental sampling. Biometrika.
• https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection
• Cekik, R., & Uysal, A. K. (2020). A novel filter feature selection method using rough set for short text data. Expert Systems with Applications, 160, 113691.
• Cekik, R., & Uysal, A. K. (2022). A new metric for feature selection on short text datasets. Concurrency and Computation: Practice and Experience, e6909.
• Cekik, R., & Telceken, S. (2018). A new classification method based on rough sets theory. Soft Computing, 22(6), 1881-1889.
• Parlak, B., & Uysal, A. K. (2021). The effects of globalisation techniques on feature selection for text classification. Journal of Information Science, 47(6), 727-739.