Impact of Text Preprocessing Techniques on SMS Spam Classification Accuracy

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.