Comparative study of naïve Bayes and SVM algorithms for text mining using natural language processing
Author(s): E Venkatesan, Guest Lecturer and V Thangavel
Abstract: This study focuses on applying Natural Language Processing (NLP) and text mining techniques for efficient text document analysis. The objective is to compare two machine learning algorithms—Naïve Bayes Classifier and Support Vector Machine (SVM) for accurate text classification and pattern recognition. Essential preprocessing techniques such as tokenization, stop-word removal, stemming, and lemmatization are applied to eliminate noise and improve text quality. Experimental results show that Naïve Bayes performs faster with lower computational cost, while SVM provides higher accuracy for complex datasets. The findings demonstrate that appropriate preprocessing and algorithm selection greatly enhance the effectiveness of NLP-based text mining applications.
E Venkatesan, Guest Lecturer, V Thangavel. Comparative study of naïve Bayes and SVM algorithms for text mining using natural language processing. Int J Cloud Comput Database Manage 2025;6(2):90-92. DOI: 10.33545/27075907.2025.v6.i2b.111
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