A study on deep learning for Vietnamese text classification

Abstract

Text categorization aims to automatically assign given text passages or documents to predetermined categories or subjects. Despite the wide array of techniques employed in classifying English text, there remains a dearth of research on Vietnamese text classification. This paper introduces a novel approach utilizing a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) with a deep network structure for Vietnamese text classification. Our findings demonstrate a substantial improvement in classification accuracy when applying deep learning techniques to two Vietnamese news corpus datasets. This study contributes to the advancement of Vietnamese text classification by introducing and demonstrating the efficacy of LSTM and CNN with a deeper network structure. The results offer valuable insights for researchers and practitioners working on text categorization in the Vietnamese language.