The aim of aspect-level sentiment analysis is to identify the sentiment polarity of a given target term in sentences. Existing neural network models provide a useful account of how to judge the polarity. However, context relative position information for the target terms is adversely ignored under the limitation of training datasets. Considering position features between words into the models can improve the accuracy of sentiment classification. Hence, this study proposes an improved classification model by combining multilevel interactive bidirectional Gated Recurrent Unit (GRU), attention mechanisms, and position features (MI-biGRU). Firstly, the position features of words in a sentence are initialized to enrich word embedding. Secondly, the approach extracts the features of target terms and context by using a well-constructed multilevel interactive bidirectional neural network. Thirdly, an attention mechanism is introduced so that the model can pay greater attention to those words that are important for sentiment analysis. Finally, four classic sentiment classification datasets are used to deal with aspect-level tasks. Experimental results indicate that there is a correlation between the multilevel interactive attention network and the position features. MI-biGRU can obviously improve the performance of classification.

中文翻译：

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基于位置特征的多层次交互式双向GRU和注意力机制的方面水平情感分析

方面级别的情感分析的目的是识别句子中给定目标词的情感极性。现有的神经网络模型为如何判断极性提供了有用的说明。但是，在训练数据集的限制下，不利地忽略了目标词的上下文相对位置信息。在模型中考虑单词之间的位置特征可以提高情感分类的准确性。因此，本研究通过结合多级交互式双向门控循环单元（GRU），注意力机制和位置特征（MI-biGRU）提出了一种改进的分类模型。首先，初始化单词在句子中的位置特征，以丰富单词的嵌入。其次，该方法通过使用结构良好的多层交互式双向神经网络提取目标词和上下文的特征。第三，引入注意机制，使模型可以更加注意那些对情感分析很重要的单词。最后，使用四个经典的情感分类数据集来处理方面级别的任务。实验结果表明，多层次互动注意力网络与位置特征之间存在相关性。MI-biGRU可以明显提高分类性能。四个经典的情感分类数据集用于处理方面级别的任务。实验结果表明，多层次互动注意力网络与位置特征之间存在相关性。MI-biGRU可以明显提高分类性能。四个经典的情感分类数据集用于处理方面级别的任务。实验结果表明，多层次互动注意力网络与位置特征之间存在相关性。MI-biGRU可以明显提高分类性能。