Topic Classification from Text Using Decision Tree, K-NN and Multinomial Naïve Bayes

One of the central motivations behind Natural Language Processing is detecting patterns. Given a text document, the task of identifying the context is known to be as topic classification. This paper explores the performance of three different classifiers namely Decision Tree, K-Nearest Neighbors and Multinomial Naive Bayes on a topic classification task (with six topic class). The evaluation is done on the basis of accuracy, precision, recall,and f1-score based results. Among those three aforementioned classifiers, we have selected the Multinomial Naïve Bayes a sour best model using which we achieved 91.8% accuracy.