A Multi-filter BiLSTM-CNN Architecture for Vietnamese Sentiment Analysis

Lac Si Le, Dang Van Thin, Ngan Luu-Thuy Nguyen, Son Quoc Trinh

Abstract: Feedback is information about reactions to a product or a person’s performance of a task. It is powerful as it serves as a guide to assist people to know how others perceive their performance and helps them meet standards. This paper concentrates on the use of natural language processing and deep learning. It combines the advantages of these approaches to perform sentiment analysis on student and customer feedback. Furthermore, word embedding is also applied to the model to add complementary effectiveness. The preliminary findings show that the use of BiLSTM-CNN–the first to catch the temporary information of the data and the second to extract the local structure thereof–outperformed other algorithms in terms of the F1-score measurement, with 93.55% for the Vietnamese Student’s Feedback Corpus (VSFC) and 84.14% for the Vietnamese Sentiment (VS). The results demonstrate that our method is an improvement compared to the best previously proposed methods on the two datasets.