An Efficient Framework For Vietnamese Sentiment Classification

With the booming development of E-commerce platforms in many counties, there is a massive amount of customers' review data in different products and services. Understanding customers' feedbacks in both current and new products can give online retailers the possibility to improve the product quality, meet customers' expectations, and increase the corresponding revenue. In this paper, we investigate the Vietnamese sentiment classification problem on two datasets containing Vietnamese customers' reviews. We propose eight different approaches, including Bi-LSTM, Bi-LSTM + Attention, Bi-GRU, Bi-GRU + Attention, Recurrent CNN, Residual CNN, Transformer, and PhoBERT, and conduct all experiments on two datasets, AIVIVN 2019 and our dataset self-collected from multiple Vietnamese e-commerce websites. The experimental results show that all our proposed methods outperform the winning solution of the competition"AIVIVN 2019 Sentiment Champion" with a significant margin. Especially, Recurrent CNN has the best performance in comparison with other algorithms in terms of both AUC (98.48%) and Fl-score (93.42%) in this competition dataset and also surpasses other techniques in our dataset collected. Finally, we aim to publish our codes, and these two datasets later to contribute to the current research community related to the field of sentiment analysis.