In this paper, we present a process of building a social listening system based on aspect-based sentiment analysis in Vietnamese from creating a dataset to building a real application. Firstly, we create UIT-ViSFD, a Vietnamese Smartphone Feedback Dataset as a new benchmark corpus built based on a strict annotation schemes for evaluating aspect-based sentiment analysis, consisting of 11,122 human-annotated comments for mobile e-commerce, which is freely available for research purposes. We also present a proposed approach based on the Bi-LSTM architecture with the fastText word embeddings for the Vietnamese aspect based sentiment task. Our experiments show that our approach achieves the best performances with the F1-score of 84.48% for the aspect task and 63.06% for the sentiment task, which performs several conventional machine learning and deep learning systems. Last but not least, we build SA2SL, a social listening system based on the best performance model on our dataset, which will inspire more social listening systems in future.
翻译:在本文中,我们展示了在越南建立基于方方面面情绪分析的社会倾听系统的过程。 首先,我们创建了越南智能手机反馈数据集UIT-VISF,这是一个越南智能手机反馈数据集,作为建立在严格的说明计划基础上的新基准,用于评价基于方方面面情绪分析,其中包括11 122条关于移动电子商务的附加说明的评论,为研究目的免费提供。我们还提出了一个基于Bi-LSTM结构的拟议方法,其中包含基于越南方方面面情绪任务的快速字嵌入字体。我们的实验表明,我们的方法取得了最佳的成绩,F1分数84.48%用于执行方面任务,F1分数为情感任务,63.06%用于开展若干常规机器学习和深层学习系统。最后但并非最不重要的是,我们建立了SA2SL,一个基于我们数据集最佳表现模型的社会倾听系统,它将在未来激励更多的社会倾听系统。