Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification

Sentiment analysis has been a well-studied research direction in computational linguistics. Deep neural network models, including convolutional neural networks (CNN) and recurrent neural networks (RNN), yield promising results on text classification tasks. RNN-based architectures, such as, long short-term memory (LSTM) and gated recurrent unit (GRU) can process sequences of any length. However, using them in the feature extraction layer of a deep neural network architecture increases the dimensionality of the feature space. In addition, such models value different features equally. To solve these issues, we propose a bidirectional convolutional recurrent neural network architecture, which utilizes two separate…