Sentiment Classification For Review Texts Via Bi-directional Gated Recurrent Unit

As one of hot spots of research in the field of natural language processing(NLP),researches on textual sentiment classification have also increased in recent years.How to effectively express the textual sentiment semantic and accurately classify the text are core issues of textual sentiment classification,while the purpose of review textual sentiment classification is to predict the overall emotional expression of users in product documents.Recent researches have found that the accuracy of review textual sentiment classification is not only related to the textual content,but also related to subject information and object information which related to the review text,such as user preferences and product characteristics.Although some document level textual sentiment classification takes into account the above information,there are the following challenges: 1.Subject information and object information related to the review text cannot be expressed effectively.2.Textual sentiment semantic representation model is too simple to express the text contextual semantic information effectively.3.The complexity of the model is too high to lead to too much training.To solve the above problems,we respectively propose three textual sentiment classification models:(1)Firstly,a model of textual sentiment classification based on attention mechanism is put forward.The construction of model first uses hierarchical long short-term memory network to extract sentence features through word features,and text features through sentence features;then uses attention mechanism to integrate subject information and object information of the comment text into the text semantic representation.(2)Then,a model of textual sentiment classification based on Bi-directional Long Short-Term Memory(Bi-LSTM)is put forward.The construction of model first uses Bi-LSTM to extract text context information;then uses hierarchical structure to extract text features;and finally uses attention mechanism to integrate subject information and object information of the comment text into the text semantic representation.(3)At last,a model of textual sentiment classification based on Bi-directional Gated Recurrent Unit(Bi-GRU)is put forward.The construction of model first uses Bi-GRU network to simplify Bi-LSTM network;then uses hierarchical structure to extract text features;and finally uses attention mechanism to integrate subject information and object information of the comment text into the text semantic representation.The experimental results shows that compared with the traditional textual sentiment classification method,the method we proposed has better classification effect,significantly improves the robustness of the text sentiment classification,at the same time can effectively simplifies the model parameters and improves the training efficiency of the model...