基于小窗口定位注意力的越南电子商务评论情感分析

Binh Le-Minh, Thi-Phuong Le, K. Tran, Khanh-Huyen Bui, Hoang-Quynh Le, Duy-Cat Can, Hung Nguyen Chung Thanh, Mai-Vu Tran
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引用次数: 3

摘要

本文演示了为解决越南电子商务评论的基于方面的情感分类而开发的系统。我们使用基于深度学习应用的监督学习模型和多个经典分类器,如随机森林、决策树、支持向量机等,来对我们的数据集进行最佳的模型排序。我们的方法获得了高达95%的微观平均和宏观平均性能。此外,我们还介绍了如何在两个领域(技术和母婴)准备我们的越南语手动注释的多方面数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Aspect-Based Sentiment Analysis Using Mini-Window Locating Attention for Vietnamese E-commerce Reviews
This article illustrates a system developed to tackle Aspect-based sentiment classification for Vietnamese E-commerce reviews. We employ supervised learning models based on Deep Learning application and multiple classic classifiers such as Random Forest, Decision Tree, Support Vector Machine, etc. to sort out the model performs best with our dataset. Our method obtained the maximum Micro-Average and Macro-Average Performance of 95%. Furthermore, we present how our Vietnamese manually-annotated multi-aspect dataset in two domains: Technology and Mother & Baby was prepared.
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