Comparative Analysis of Text Classification using SVM, Naïve Bayes, and KNN Models

In contemporary times, there has indeed been a substantial rise in the count of complicated texts and documents that demand a greater comprehension of Machine Learning (ML) approaches to identify texts in numerous areas effectively. Several ML algorithms have produced outstanding Natural Language Processing achievements. The ability of these learning techniques to comprehend complicated systems and non-linear correlations within the information is critical to their effectiveness. On the other hand, identifying appropriate patterns, architectures, and algorithms for text classification is difficult for scientists. Thus, this study comprehensively analyzes text classification ML techniques like K-nearest neighbor, Naive Bayes, and Support Vector Machine. Lastly, the shortcomings of every method are examined, as well as their use in real-world challenges.