Deep learning model with hierarchical attention mechanism for sentiment classification of Vietnamese comments

Abstract

In the current digital era, text documents become valuable for businesses to reach potential customers and curtail advertising costs. However, extracting and classifying beneficial information from texts can prove challenging and time-consuming, particularly in complex languages like Vietnamese. This study aims to classify the sentiment of Vietnamese comments on e-commerce websites into negative and positive classes. To enhance the performance of sentiment classification, the study fine-tuned traditional models of Convolutional Neural Networks and Recurrent Neural Networks (RNN). Then, this research proposed a combination of RNN and attention mechanisms at the word and word-and-sentence levels of the input document. The results showed an impressive accuracy of 93.72% and an F1 score of 93.7% on the RNN model with a word-and-sentence-level attention mechanism. This research outcome contributes to the field of text classification and could be applied in opinion mining, customer feedback analysis, and natural language processing. Future work aims to enhance sentiment analysis accuracy and expand the models’ scope to encompass more languages.