Attention-based bidirectional gated recurrent unit neural networks for sentiment analysis

Abstract

Sentiment analysis is an important research direction of natural language processing. In-depth exploration of online textual emotional information has great social significance social and commercial value for market research, online public opinion discovery and early warning. In this paper, the gated recurrent unit neural network and the attention mechanism are combined to propose a text sentiment analysis model---Attention-BGRU. The attention mechanism was added to the gated recurrent unit neural network, and the model was implemented under the Keras deep learning framework. According to the experimental results, the comparison with the existing models shows that the proposed model has obvious advantages over the general deep learning method.