Comparing SVM and Naive Bayes classifiers for text categorization with Wikitology as knowledge enrichment

TL;DR

This study compares SVM and Naive Bayes classifiers for text categorization enhanced with Wikitology knowledge, finding Naive Bayes performs significantly better with external knowledge enrichment.

Contribution

It demonstrates that Naive Bayes outperforms SVM in text classification tasks when using Wikitology for knowledge enrichment.

Findings

Naive Bayes improves by +28.78% with Wikitology.

SVM improves by +6.36% with Wikitology.

Naive Bayes is preferable for external knowledge-based text classification.

Abstract

The activity of labeling of documents according to their content is known as text categorization. Many experiments have been carried out to enhance text categorization by adding background knowledge to the document using knowledge repositories like Word Net, Open Project Directory (OPD), Wikipedia and Wikitology. In our previous work, we have carried out intensive experiments by extracting knowledge from Wikitology and evaluating the experiment on Support Vector Machine with 10- fold cross-validations. The results clearly indicate Wikitology is far better than other knowledge bases. In this paper we are comparing Support Vector Machine (SVM) and Na\"ive Bayes (NB) classifiers under text enrichment through Wikitology. We validated results with 10-fold cross validation and shown that NB gives an improvement of +28.78%, on the other hand SVM gives an improvement of +6.36% when compared with baseline results. Na\"ive Bayes classifier is better choice when external enriching is used through any external knowledge base.