ABSTRACT In order to explore the impact of different memory modules under the framework of memory network for aspect level sentiment classification, we use convolutional neural networks (CNN) and bidirectional long short term memory (BiLSTM) to design four kinds of memory network models in this paper. The first model uses CNN for building a memory module, which is capable of capturing local information in documents. The second uses BiLSTM for building another memory module, which captures sequence information in documents. At the same time, the following two models use CNN and BiLSTM for building memory module--one builds a hierarchical neural network by using CNN and BiLSTM for building memory module, which combines both local and sequence information together. The other, respectively, uses CNN and BiLSTM for building two memory modules, respectively, which captures local information and sequence information through different modules. And then, we combine the final representations, which are generated from the different memory networks, for further sentiment classification. Experiments on laptop and restaurant datasets demonstrate that our four methods achieving better results than MemNet. In particular, the latter two models achieve better performance than the first two models and feature-based support vector machine approach. INDEX TERMS Sentiment classification, deep memory network, convolutional neural networks, bidirectional long short term memory.
Han et al. REF used CNN and BLSTM to design four kinds of memory network models for text sentiment classification.
Attention-Based Memory Network for Text Sentiment Classification
IEEE Access
IEEE Access
Computer Science