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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >Attention Mechanism with Gated Recurrent Unit Using Convolutional Neural Network for Aspect Level Opinion Mining
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Attention Mechanism with Gated Recurrent Unit Using Convolutional Neural Network for Aspect Level Opinion Mining

机译：使用卷积神经网络对各个方面意见挖掘的关注机制

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摘要

Deep neural network models are emerging in the area of natural language processing and have become a topic of interest in sentiment analysis. The participation of more social media users provides increased information which has made analysis challenging. Aspect level sentiment analysis is used in the identification of the sentiment polarity of a text in different aspects. This paper presents four deep neural network-based methods with varied input word vector representation for the aspect level opinion mining. A novel approach using an attention mechanism with a gated recurrent unit and a convolutional neural network for aspect level opinion mining with different input vector representations is proposed. This work is an addition to the existing research that includes novel approaches for the assessment of the quality of services based on customer reviews in the restaurant domain. Data accumulated on restaurant opinion have been chosen for experimental study, and the results obtained indicate achievement of good accuracy, precision, recall, and f-measure compared to other approaches.
机译：深度神经网络模型正在出现在自然语言处理领域，并成为情感分析的兴趣。更多社交媒体用户的参与提供了增加的信息，这使得分析具有挑战性。方面情绪分析用于识别不同方面的文本的情感极性。本文介绍了四种基于神经网络的基于神经网络的方法，其中各种意见挖掘的各种输入字矢量表示。提出了一种新的方法，采用带有所通用的经常性单元和具有不同输入向量表示的方面意见挖掘的各个复发单元和卷积神经网络的一种新方法。这项工作是现有研究的补充，其中包括基于餐厅域的客户评论评估服务质量的新方法。在餐厅意见中累积的数据已选择进行实验研究，并获得了与其他方法相比的良好准确性，精度，召回和F测量的结果。

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