Hybrid approach for text categorization: A case study with Bangla news article

Abstract

The incredible expansion of online texts due to the Internet has intensified and revived the interest of sorting, managing and categorising the documents into their respective domains. This shows the pressing need for automatic text categorization system to assign a document into its appropriate domain. In this article, the focus is on showcasing the effectiveness of a hybrid approach that works elegantly by combining text-based and graph-based features. The hybrid approach was applied on 14,373 Bangla articles with 57,22,569 tokens collected from various online news corpora covering nine categories. This article also presents the individual application of both the features to explicate how they generally work. For classification purposes, the feature sets were passed through the Bayesian classification methods which yield satisfactory results with 98.73% accuracy for Naïve Bayes Multinomial (NBM). Also, to test the robustness and language independency of the system, the experiments were performed on two popular English datasets as well.

American literature news narration based on computer web technology

“…They found that CBOW-CNN using word2vec obtained the best accuracy of 93.41%, which significantly improved the accuracy of Tigrinya news classification [ 37 ]. Dhar et al employed Bayes classification to classify documents with feature sets and found that the accuracy of naive Bayes polynomials was 98.73% [ 38 ]. Qing et al extracted features from sentences in sentence representation, and bidirectional gated recursive units were used to access previous and next sentence features.…”