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arXiv · 2110.07833

Span Detection for Aspect-Based Sentiment Analysis in Vietnamese

Kim Thi-Thanh Nguyen·Sieu Khai Huynh·Luong Luc Phan·Phuc Huynh Pham·Duc-Vu Nguyen·Kiet Van Nguyen
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Abstract

Aspect-based sentiment analysis plays an essential role in natural language processing and artificial intelligence. Recently, researchers only focused on aspect detection and sentiment classification but ignoring the sub-task of detecting user opinion span, which has enormous potential in practical applications. In this paper, we present a new Vietnamese dataset (UIT-ViSD4SA) consisting of 35,396 human-annotated spans on 11,122 feedback comments for evaluating the span detection in aspect-based sentiment analysis. Besides, we also propose a novel system using Bidirectional Long Short-Term Memory (BiLSTM) with a Conditional Random Field (CRF) layer (BiLSTM-CRF) for the span detection task in Vietnamese aspect-based sentiment analysis. The best result is a 62.76% F1 score (macro) for span detection using BiLSTM-CRF with embedding fusion of syllable embedding, character embedding, and contextual embedding from XLM-RoBERTa. In future work, span detection will be extended in many NLP tasks such as constructive detection, emotion recognition, complaint analysis, and opinion mining. Our dataset is freely available at https://github.com/kimkim00/UIT-ViSD4SA for research purposes.

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Kim Thi-Thanh Nguyen, Sieu Khai Huynh, Luong Luc Phan, Phuc Huynh Pham, Duc-Vu Nguyen, Kiet Van Nguyen. 2021-10-15. Span Detection for Aspect-Based Sentiment Analysis in Vietnamese. https://arxiv.org/abs/2110.07833

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