Text sentiment classification is of critical importance to improve the autonomous decision making and communication ability among object peers in the Social Internet of Things (SIoT). To classify sentiment polarity on a fine-grained level, aspect-level sentiment classification has become a promising direction in recent years. However, the existing solutions typically ignore the mutual information between sentences and their respective aspect terms while generally performing sentiment classification by using the simple attention mechanism. Thus, the relevant results seem to be unpromising. We aim to develop a novel neural-network-based model, by relying on the natural language processing model for rich feature extraction, called mutual attention neural networks (MANs), to conduct the aspect-level sentiment classification tasks in this article. Compared with the previous work, our proposed MAN model takes advantage of the bidirectional long short-term memory (Bi-LSTM) networks to obtain semantic dependence of sentences and their respective aspect terms, while learning the sentiment polarities of aspect terms in sentences by proposing the mutual attention mechanism. To evaluate the performance of MAN, we conduct our experiments on three real-world data sets, i.e., LAPTOP, REST, and TWITTER. The experimental results show that our proposed MAN model has significant performance improvements when compared to several existing models, in terms of aspect-level sentiment classification.

中文翻译：

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MAN：用于SIoT中方面级别情感分类的相互注意神经网络模型

文本情感分类对于提高社交物联网（SIoT）中对象同级之间的自主决策和交流能力至关重要。为了在细粒度级别上对情感极性进行分类，近年来，方面级别的情感分类已成为一个有前途的方向。但是，现有的解决方案通常会忽略句子和它们各自的方面术语之间的相互信息，而通常通过使用简单的注意机制来执行情感分类。因此，相关结果似乎没有希望。我们旨在通过依靠自然语言处理模型来进行丰富的特征提取（称为相互关注神经网络（MAN））来开发基于神经网络的新型模型，以执行本文中的方面级情感分类任务。与先前的工作相比，我们提出的MAN模型利用双向长短期记忆（Bi-LSTM）网络来获得句子及其各个方面术语的语义依赖性，同时通过提出学习句子中方面术语的情感极性的方法相互关注的机制。为了评估MAN的性能，我们对三个真实数据集（LAPTOP，REST和TWITTER）进行了实验。实验结果表明，与现有的几种模型相比，我们提出的MAN模型在方面方面的情感分类方面具有显着的性能改进。通过提出相互注意的机制来学习句子中方面术语的情感极性。为了评估MAN的性能，我们对三个真实数据集（LAPTOP，REST和TWITTER）进行了实验。实验结果表明，与现有的几种模型相比，我们提出的MAN模型在方面方面的情感分类方面具有显着的性能改进。通过提出相互注意的机制来学习句子中方面术语的情感极性。为了评估MAN的性能，我们对三个真实数据集（LAPTOP，REST和TWITTER）进行了实验。实验结果表明，与现有的几种模型相比，我们提出的MAN模型在方面方面的情感分类方面具有显着的性能改进。