Impact of Text Preprocessing Techniques on SMS Spam Classification Accuracy
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I1P112Keywords:
SMS Spam Classification, Text Preprocessing, Natural Language Processing, Machine Learning, Feature Engineering, Classification Accuracy, Spam DetectionAbstract
Text preprocessing plays a critical role in enhancing the performance of SMS spam classification systems by transforming raw text into a structured and machine-readable format. This study examines the impact of various text preprocessing techniques on the accuracy of SMS spam classification models. Key preprocessing steps analyzed include text normalization, tokenization, stop-word removal, stemming, lemmatization, handling of special characters, and feature scaling. Using benchmark SMS spam datasets, multiple machine learning classifiers are evaluated under different preprocessing configurations to assess their influence on classification accuracy, precision, recall, and F1-score. The results demonstrate that appropriate preprocessing significantly improves model performance by reducing noise, dimensionality, and data sparsity. However, the study also highlights that excessive or improper preprocessing can lead to information loss and reduced accuracy. The findings provide practical insights into selecting optimal preprocessing pipelines for efficient and accurate SMS spam detection systems, particularly in resource-constrained and real-time environments.
References
[1] Narra, B., Buddula, D. V. K. R., Patchipulusu, H., Vattikonda, N., Gupta, A., & Polu, A. R. (2024). The integration of artificial intelligence in software development: Trends, tools, and future prospects. Available at SSRN 5596472.
[2] Gupta, A. K., Polu, A. R., Narra, B., Buddula, D. V. K. R., Patchipulusu, H. H. S., & Vattikonda, N. (2024). Leveraging deep learning models for intrusion detection systems for secure networks. Journal of Computer Science and Technology Studies, 6(2), 199-208.
[3] Achuthananda, R. P., Bhumeka, N., Dheeraj Varun Kumar, R. B., Hari Hara, S. P., & Navya, V. (2024). Evaluating machine learning approaches for personalized movie recommendations: A comprehensive analysis. J Contemp Edu Theo Artific Intel: JCETAI-115.
[4] Polu, A. R., Narra, B., Buddula, D. V. K. R., Hara, H., Patchipulusu, S., Vattikonda, N., & Gupta, A. K. Analyzing The Role of Analytics in Insurance Risk Management: A Systematic Review of Process Improvement and Business Agility.
[5] Tamilmani, V., Maniar, V., Singh, A. A., Kothamaram, R. R., Rajendran, D., & Namburi, V. D. (2024). A Review of Cyber Threat Detection in Software-Defined and Virtualized Networking Infrastructures. International Journal of Technology, Management and Humanities, 10(04), 136-146.
[6] Kothamaram, R. R., Rajendran, D., Namburi, V. D., Tamilmani, V., Maniar, V., & Singh, A. A. S. Predictive Analytics for Customer Retention in Telecommunications Using ML Techniques.
[7] Singh, A. A. S., Kothamaram, R. R., Rajendran, D., Deepak, V., Namburi, V. T., & Maniar, V. A Review on Model-Driven Development with a Focus on Microsoft PowerApps.
[8] Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, J. V., Attipalli, A., & Enokkaren, S. J. (2024). A Survey on Blockchain-Enabled ERP Systems for Secure Supply Chain Processes and Cloud Integration. International Journal of Technology, Management and Humanities, 10(04), 126-135.
[9] Waditwar, P. (2024) AI for Bathsheba Syndrome: Ethical Implications and Preventative Strategies. Open Journal of Leadership, 13, 321-341. doi: 10.4236/ojl.2024.133020
[10] Mamidala, J. V., Bitkuri, V., Attipalli, A., Kendyala, R., Kurma, J., & Enokkaren, S. J. (2024). Machine Learning Approaches to Salary Prediction in Human Resource Payroll Systems. Journal of Computer Science and Technology Studies, 6(5), 341-349.
[11] Attipalli, A., Kendyala, R., Kurma, J., Mamidala, J. V., Bitkuri, V., & Enokkaren, S. J. Privacy Preservation in the Cloud: A Comprehensive Review of Encryption and Anonymization Methods. International Journal of Multidisciplinary on Science and Management IJMSM, 1(1).
[12] Enokkaren, S. J., Kendyala, R., Kurma, J., Mamidala, J. V., Bitkuri, V., & Attipalli, A. Artificial Intelligence (AI)-Based Advance Models for Proactive Payroll Fraud Detection and Prevention.
[13] Gangineni, V. N., Tyagadurgam, M. S. V., Pabbineedi, S., Penmetsa, M., Bhumireddy, J. R., & Chalasani, R. (2024). AI-Powered Cybersecurity Risk Scoring for Financial Institutions Using Machine Learning Techniques (Approved by ICITET 2024). Journal of Artificial Intelligence & Cloud Computing.
[14] Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, J. V., Enokkaren, S. J., & Attipalli, A. (2022). Empowering Cloud Security with Artificial Intelligence: Detecting Threats Using Advanced Machine learning Technologies. International Journal of AI, BigData, Computational and Management Studies, 3(4), 49-59.
[15] Attipalli, A., Mamidala, J. V., KURMA, J., Bitkuri, V., Kendyala, R., & Enokkaren, S. (2022). Towards the Efficient Management of Cloud Resource Allocation: A Framework Based on Machine Learning. Available at SSRN 5741265.
[16] Enokkaren, S. J., Attipalli, A., Bitkuri, V., Kendyala, R., Kurma, J., & Mamidala, J. V. (2022). A Deep-Review based on Predictive Machine Learning Models in Cloud Frameworks for the Performance Management. Universal Library of Engineering Technology, (Issue).
[17] Kurma, J., Mamidala, J. V., Attipalli, A., Enokkaren, S. J., Bitkuri, V., & Kendyala, R. (2022). A Review of Security, Compliance, and Governance Challenges in Cloud-Native Middleware and Enterprise Systems. International Journal of Research and Applied Innovations, 5(1), 6434-6443.
[18] Attipalli, A., Enokkaren, S., KURMA, J., Mamidala, J. V., Kendyala, R., & BITKURI, V. (2022). A Deep-Review based on Predictive Machine Learning Models in Cloud Frameworks for the Performance Management. Available at SSRN 5741282.
[19] Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, J. V., Enokkaren, S. J., & Attipalli, A. (2022). Empowering Cloud Security with Artificial Intelligence: Detecting Threats Using Advanced Machine learning Technologies. International Journal of AI, BigData, Computational and Management Studies, 3(4), 49-59.
[20] Chalasani, R., Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., Penmetsa, M., & Bhumireddy, J. R. (2022). Leveraging big datasets for machine learning-based anomaly detection in cybersecurity network traffic. Available at SSRN 5538121.
[21] Chundru, S. K., Vangala, S. R., Polam, R. M., Kamarthapu, B., Kakani, A. B., & Nandiraju, S. K. K. (2022). Efficient machine learning approaches for intrusion identification of DDoS attacks in cloud networks. Available at SSRN 5515262.
[22] Chalasani, R., Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., Penmetsa, M., & Bhumireddy, J. R. (2022). Leveraging big datasets for machine learning-based anomaly detection in cybersecurity network traffic. Available at SSRN 5538121.
[23] Sandeep Kumar, C., Srikanth Reddy, V., Ram Mohan, P., Bhavana, K., & Ajay Babu, K. (2022). Efficient Machine Learning Approaches for Intrusion Identification of DDoS Attacks in Cloud Networks. J Contemp Edu Theo Artific Intel: JCETAI/101.
[24] Namburi, V. D., Singh, A. A. S., Maniar, V., Tamilmani, V., Kothamaram, R. R., & Rajendran, D. (2023). Intelligent Network Traffic Identification Based on Advanced Machine Learning Approaches. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 118-128.
[25] Rajendran, D., Maniar, V., Tamilmani, V., Namburi, V. D., Singh, A. A. S., & Kothamaram, R. R. (2023). CNN-LSTM Hybrid Architecture for Accurate Network Intrusion Detection for Cybersecurity. Journal Of Engineering And Computer Sciences, 2(11), 1-13.
[26] Kothamaram, R. R., Rajendran, D., Namburi, V. D., Tamilmani, V., Singh, A. A., & Maniar, V. (2023). Exploring the Influence of ERP-Supported Business Intelligence on Customer Relationship Management Strategies. International Journal of Technology, Management and Humanities, 9(04), 179-191.
[27] Singh, A. A. S. S., Mania, V., Kothamaram, R. R., Rajendran, D., Namburi, V. D. N., & Tamilmani, V. (2023). Exploration of Java-Based Big Data Frameworks: Architecture, Challenges, and Opportunities.Journal of Artificial Intelligence & Cloud Computing,2(4), 1-8.
[28] Waditwar, P. (2024) The Intersection of Strategic Sourcing and Artificial Intelligence: A Paradigm Shift for Modern Organizations. Open Journal of Business and Management, 12, 4073-4085. doi: 10.4236/ojbm.2024.126204
[29] Almeida, T. A., Gómez Hidalgo, J. M., & Yamakami, A. (2011). Contributions to the study of SMS spam filtering: New collection and results. Proceedings of the 11th ACM Symposium on Document Engineering, 259–262.
[30] Forman, G. (2003). An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research, 3, 1289–1305.
[31] Gómez Hidalgo, J. M., Bringas, G. C., Sánz, E. P., & García, F. C. (2006). Content based SMS spam filtering. Proceedings of the 2006 ACM Symposium on Document Engineering, 107–114.
[32] Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. European Conference on Machine Learning, 137–142. Springer.
[33] Kowsari, K., Meimandi, K. J., Heidarysafa, M., Mendu, S., Barnes, L., & Brown, D. (2019). Text classification algorithms: A survey. Information, 10(4), 150.
[34] Ramos, J. (2003). Using TF-IDF to determine word relevance in document queries. Proceedings of the First Instructional Conference on Machine Learning, 133–142.
[35] Sahami, M., Dumais, S., Heckerman, D., & Horvitz, E. (1998). A Bayesian approach to filtering junk e-mail. Learning for Text Categorization: Papers from the 1998 Workshop, 55–62.
[36] Chundru, S. K., Vikram, M. S., Naidu, V., Pabbineedi, S., Kakani, A. B., & Nandiraju, S. K. K. Analyzing and Predicting Anaemia with Advanced Machine Learning Techniques with Comparative Analysis.
[37] Polam, R. M., Kamarthapu, B., Penmetsa, M., Bhumireddy, J. R., Chalasani, R., & Vangala, S. R. (2025). Advanced Machine Learning for Robust Botnet Attack Detection in Evolving Threat Landscapes. Available at SSRN 5515384.
[38] Kamarthapu, B., Penmetsa, M., Bhumireddy, J. R., Chalasani, R., Vangala, S. R., & Polam, R. M. (2025). Data-Driven Detection of Network Threats using Advanced Machine Learning Techniques for Cybersecurity. Available at SSRN 5515400.
[39] Penmetsa, M., Bhumireddy, J. R., Chalasani, R., Vangala, S. R., Polam, R. M., & Kamarthapu, B. (2025). Effectiveness of Deep Learning Algorithms in Phishing Attack Detection for Cybersecurity Frameworks. Available at SSRN 5515385.
[40] Vanaparthi, N. R. (2025). Why digital transformation in fintech requires mainframe modernization: A cost benefit analysis. International Journal of Science and Research Archive, 14(1), 1052–1062. https://doi.org/10.30574/ijsra.2025.14.1.0161
[41] Kamarthapu, B., Penmetsa, M., Vangala, S. R., & Polam, R. M. (2025). Effectiveness of Deep Learning Algorithms in Phishing Attack Detection for Cybersecurity Frameworks. Available at SSRN 5571241.
[42] Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., Vangala, S. R., Polam, R. M., & Kamarthapu, B. (2025). Leveraging NLP and Sentiment Analysis for ML-Based Fake News Detection with Big Data. Available at SSRN 5515418.
[43] Gangineni, V. N., Penmetsa, M., Bhumireddy, J. R., Chalasani, R., & Tyagadurgam, M. SV, & Pabbineedi, S.(2025). Big Data and Predictive Analytics for Customer Retention: Exploring the Role of Machine Learning in E-Commerce.
[44] Prajkta Waditwar. Quantum-Enhanced Travel Procurement: Hybrid Quantum–Classical Optimization for Enterprise Travel Management. World Journal of Advanced Engineering Technology and Sciences, 2025, 17(03), 375-386. Article DOI: https://doi.org/10.30574/wjaets.2025.17.3.1572.
[45] Vanaparthi, N. R. (2025). Regulatory compliance in the digital age: How mainframe modernization can support financial institutions. International Journal of Research in Computer Applications and Information Technology, 8(1), 383–396. https://doi.org/10.34218/IJRCAIT_08_01_033
[46] Waditwar, P. (2025) AI-Driven Procurement in Ayurveda and Ayurvedic Medicines & Treatments. Open Journal of Business and Management, 13, 1854-1879. doi: 10.4236/ojbm.2025.133096
[47] Vanaparthi, N. R. (2025). The roadmap to mainframe modernization: Bridging legacy systems with the cloud. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 125–133. https://doi.org/10.32628/CSEIT25111214
[48] Prabakar, D., Iskandarova, N., Iskandarova, N., Kalla, D., Kulimova, K., & Parmar, D. (2025, May). Dynamic Resource Allocation in Cloud Computing Environments Using Hybrid Swarm Intelligence Algorithms. In 2025 International Conference on Networks and Cryptology (NETCRYPT) (pp. 882-886). IEEE.
[49] Nagaraju, S., Johri, P., Putta, P., Kalla, D., Polvanov, S., & Patel, N. V. (2025, May). Smart Routing in Urban Wireless Ad Hoc Networks Using Graph Attention Network-Based Decision Models. In 2025 International Conference on Networks and Cryptology (NETCRYPT) (pp. 212-216). IEEE.
[50] Kalla, D., Mohammed, A. S., Boddapati, V. N., Jiwani, N., & Kiruthiga, T. (2024, November). Investigating the Impact of Heuristic Algorithms on Cyberthreat Detection. In 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT) (Vol. 1, pp. 450-455). IEEE.
[51] Vadisetty, R., Polamarasetti, A., & Kalla, D. (2025, February). Automated AI-Driven Phishing Detection and Countermeasures for Zero-Day Phishing Attacks. In International Ethical Hacking Conference (pp. 285-303). Singapore: Springer Nature Singapore.
[52] Nagrath, P., Saini, I., Zeeshan, M., Komal, Komal, & Kalla, D. (2025, June). Predicting Mental Health Disorders with Variational Autoencoders. In International Conference on Data Analytics & Management (pp. 38-51). Cham: Springer Nature Switzerland.
[53] Polam, R. M., Kamarthapu, B., Penmetsa, M., Bhumireddy, J. R., Chalasani, R., & Vangala, S. R. (2025). Advanced Machine Learning for Robust Botnet Attack Detection in Evolving Threat Landscapes. Available at SSRN 5515384.
[54] Kamarthapu, B., Penmetsa, M., Bhumireddy, J. R., Chalasani, R., Vangala, S. R., & Polam, R. M. (2025). Data-Driven Detection of Network Threats using Advanced Machine Learning Techniques for Cybersecurity. Available at SSRN 5515400.
[55] Penmetsa, M., Bhumireddy, J. R., Chalasani, R., Vangala, S. R., Polam, R. M., & Kamarthapu, B. (2025). Effectiveness of Deep Learning Algorithms in Phishing Attack Detection for Cybersecurity Frameworks. Available at SSRN 5515385.
[56] Nandiraju, S. K. K., Chundru, S. K., Vangala, S. R., Polam, R. M., Kamarthapu, B., & Kakani, A. B. (2025). Towards Early Forecast of Diabetes Mellitus via Machine Learning Systems in Healthcare. European Journal of Technology, 9(1), 35-50.
[57] Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Vangala, S. R. (2025). Predictive Modeling for Property Insurance Premium Estimation Using Machine Learning Algorithms. Available at SSRN 5515382.
[58] Nandiraju, S. K. K., & Chundru, S. K. Enhancing Cybersecurity: Zero-Day.
[59] Prajkta Waditwar. Agentic AI and sustainable procurement: Rethinking anti-corrosion strategies in oil and gas. World Journal of Advanced Research and Reviews, 2025, 27(03), 1591-1598. Article DOI: https://doi.org/10.30574/wjarr.2025.27.3.3298.
[60] Vadisetty, R., Polamarasetti, A., Varadarajan, V., Kalla, D., & Ramanathan, G. K. (2025, May). Cyber Warfare and AI Agents: Strengthening National Security Against Advanced Persistent Threats (APTs). In International Conference on Intelligence-Based Transformations of Technology and Business Trends (pp. 578-587). Cham: Springer Nature Switzerland.
