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.

翻译：以视觉为基础的情绪分析在自然语言处理和人工智能中发挥着必不可少的作用。最近,研究人员只注重探测和情绪分类,而忽略了探测用户意见范围这一具有巨大实际应用潜力的子任务。在本文件中,我们提出了一个新的越南数据集(UIT-VisD4SA),由35 396人组成,其中附有35 396人的附加注释,涉及11 122份反馈意见,用于在基于情感分析方面评估跨度检测。此外,我们还提议建立一个新型系统,使用双向短期短期记忆(BILSTM),并配有有条件随机场层(BILSTM-CRF),用于越南基于侧面情绪的情绪分析中的跨度探测任务。最佳结果是62.76%的F1分(macro)用于使用BILSTM-CRF的跨度探测,并嵌入了XLM-ROBERTA。在未来的工作中,将扩展许多NLP任务的探测范围,例如建设性的探测、情感识别、投诉分析、以及意见采矿等任务。我们的数据可自由获取到 MASDM/VIMST。