Comparative Study of CNN and LSTM based Attention Neural Networks for Aspect-Level Opinion Mining
Comparative Study of CNN and LSTM based Attention Neural Networks for Aspect-Level Opinion Mining
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DOI：
10.1109/bigdata.2018.8622150
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发表时间：
2018-12
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影响因子：
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通讯作者：
Wei Quan;Zheng Chen;Jianliang Gao;Xiaohua Hu
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文献类型：
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作者：
Wei Quan;Zheng Chen;Jianliang Gao;Xiaohua Hu
摘要：观点挖掘的目的是发现和汇总关于观点目标的观点.以前的工作已经证明，在周围的背景下，意见目标的精确建模可以提高性能。然而，如何有效地学习隐藏词的语义，更好地表示目标和上下文仍然需要进一步研究。在本文中，我们提出并比较了两个交互式注意神经网络的方面级意见挖掘，一个采用两个双向长短期记忆（BLSTM），另一个采用两个卷积神经网络（CNN）。这两个框架分别学习的意见目标和上下文，其次是一个注意力机制，集成了隐藏的状态，从目标和上下文。我们将我们的模型与两个SemEval 2014测试仪上的最新基线进行比较1。实验结果表明，我们的模型在两个数据集上都获得了与基线相比具有竞争力的性能。我们的工作有助于改进最先进的方面级意见挖掘方法，并提供了一种新的方法来支持人类决策过程的基础上意见挖掘结果。我们工作中的定量和定性比较旨在为类似任务中的神经网络选择提供基本指导。
Aspect-level opinion mining aims to find and aggregate opinions on opinion targets. Previous work has demonstrated that precise modeling of opinion targets within the surrounding context can improve performances. However, how to effectively and efficiently learn hidden word semantics and better represent targets and the context still needs to be further studied. In this paper, we propose and compare two interactive attention neural networks for aspect-level opinion mining, one employs two bi-directional Long-Short-Term-Memory (BLSTM) and the other employs two Convolutional Neural Networks (CNN). Both frameworks learn opinion targets and the context respectively, followed by an attention mechanism that integrates hidden states learned from both the targets and context. We compare our model with state-of-the-art baselines on two SemEval 2014 datasets1. Experiment results show that our models obtain competitive performances against the baselines on both datasets. Our work contributes to the improvement of state-of-the-art aspect-level opinion mining methods and offers a new approach to support human decision-making process based on opinion mining results. The quantitative and qualitative comparisons in our work aim to give basic guidance for neural network selection in similar tasks.