Sentiment Classification of Reviews Based on BiGRU Neural Network and Fine-grained Attention

Abstract

Text sentiment analysis is part and parcel of natural language processing. The task of sentiment classification is actually the process of feature extraction through models. The comment text of commodities is very different from the ordinary text. The comment text has no fixed grammar and writing format and the sentiment feature information is scattered in various places of text. Due to these factors, model learning of sentiment classification is becoming increasingly complex. The paper aims at establishing a fine-grained feature extraction model based on BiGRU and attention. Firstly, the vocabulary is vectorized by means of the skip-gram model. Then, according to the pre-trained word vector, the sentiment words list can be reached and noise filtering would be conducted by Naive Bayes algorithm. Finally, the model extracts features using BiGRU and fine-grained attentions. Based on the hypothesis that a long review may lead to feature differentiation, a fine-grained attention model is proposed. In this model, the attention layer is design to focus on the feature in different level such as word level, sentence level and paragraph level. This paper validate the proposed model on two sentiment corpus JD reviews and IMDB. Empirical results show that the FGAtten-BiGRU model achieves state of the art results on sentiment analysis tasks.