Detecção de Categorias de Aspectos Utilizando Redes Neurais Profundas em Avaliações Online

Bruno Á . Souza, Alice A. F. Menezes, C. Figueiredo, Fabíola G. Nakamura, E. Nakamura
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引用次数: 1

Abstract

Virtual environments such as online stores (e.g. Amazon, Google Play and Booking) adopt a collaborative strategy of evaluation and reputation, where users classify products and services. User's opinion represents the satisfaction level of a rated item. The set of ratings of an item is a reference to its reputation/quality. Therefore, the automatic identification of a usersatisfaction related to an item, considering its textual evaluation, is a tool with singular economic potential. With deep learning researches evolution in sentiment analysis based in aspects, opportunities to apply several neural networks in this context arisen. However, the data representation models applied in these works focus only on Embeddings pre-trained networks as a way to perform feature extraction. In this way, this work aims to present a comparison between data representation techniques and deep networks approaches, to analyze which of them have better results in classifying categories of aspects. Thus, we can seethat TF-IDF with a Convolution Neural Network (CNN) had an F1 measure of 0.93%, being at least 0.02% higher than the others approaches applied in this work.
在在线评估中使用深度神经网络检测方面类别
虚拟环境,如在线商店(如Amazon, Google Play和Booking)采用评价和声誉的协作策略,用户对产品和服务进行分类。用户的意见代表了被评价项目的满意程度。一个项目的评级集是对其声誉/质量的参考。因此,考虑到其文本评价,用户满意度的自动识别是一种具有独特经济潜力的工具。随着深度学习研究在情感分析方面的发展,在此背景下应用多种神经网络的机会出现了。然而,这些工作中应用的数据表示模型只关注嵌入预训练网络作为执行特征提取的一种方式。通过这种方式,本工作旨在对数据表示技术和深度网络方法进行比较,分析哪一种方法在分类方面的类别方面效果更好。因此,我们可以看到,使用卷积神经网络(CNN)的TF-IDF具有0.93%的F1度量,比本工作中应用的其他方法至少高出0.02%。
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