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Bài báo - Tạp chí
464 (2022) Trang: 259–272
Tạp chí: Lecture Notes in Networks and Systems

With the rapid development of social media platforms as well as the current pandemic, the majority of activities are performed online. The user comments obtained from the digital channels are crucial in order that the agencies or organizations can improve and develop their brand. Thus, an automatic system is necessary to analyze the sentiment of a customer feedback. Recently, the well-known pre-trained language models for Vietnamese (PhoBERT) have achieved high performance in comparison with other approaches. However, this method may not focus on the local information in the sentiment like phrases or fragments. In this paper, we propose a PhoBERT-based convolutional neural networks (CNN) for text classification. The output of contextualized embeddings of the PhoBERT’s last four layers is fed into the CNN. This makes the network capable of obtaining more local information from the text. Besides, the PhoBERT output is also given to the transformer encoder layers in order to employ the self-attention technique, and this also makes the model more focused on the important information of the text segments. The experimental results demonstrate that the proposed approach gives competitive performance compared to the existing studies on three public datasets with Vietnamese texts.

Các bài báo khác
22 (2023) Trang: https://worldscientific.com/doi/10.1142/S1469026823500165
Tạp chí: International Journal of Computational Intelligence and Applications
1863 (2023) Trang:
Tạp chí: Communications in Computer and Information Science
2 (2021) Trang:
Tạp chí: Proceedings of Sixth International Congress on Information and Communication Technology - ICICT 2021
23 (2020) Trang: 219–239
Tạp chí: International Journal on Document Analysis and Recognition (IJDAR)
(2020) Trang:
Tạp chí: The 2020 12th International Conference on Knowledge and Systems Engineering (KSE), tổ chức từ ngày 12 - 14/11/2020 tại Đại học Cần Thơ
 


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