Document-Level Sentiment Analysis Of Deep Learning Incorporating Topic Features

The rapid development of the Internet has promoted the rise of platforms such as social and e-commerce.A large number of comment texts have emerged on the Internet,and there is necessary for sentiment analysis technology to automatically analyze text.For document-level sentiment analysis,scholars have proposed a variety of methods of text analysis based on neural network,and the accuracy of text analysis has been continuously improved a lot,however,there are still some shortcomings:The emotional information used in the existing emotional word vector model is not rich enough,and document features are not effectively extracted.At the same time,lacking attention to the context of the text,making the effect of emotional classification poor.Therefore,the paper proposes a document-level method for sentiment analysis based on topic features and deep learning.The specific research content is divided into the following three aspects:(1)In view of the fact that the emotional information used in the existing emotional word vector model is not rich enough.So the learning model of sentiment word vector based on Skip-gram are constructed,and the improved Skip-gram model and the asymmetric convolutional neural network are combined into the SA-SWVM model to obtain emotional word vector including rich semantic and emotional information.Experiments were carried out at the level of words and sentences,which verified that the constructed emotional word vector model has good adaptability under Chinese and English datasets,and can capture emotional information in the corpus.(2)In view of the fact that document features are not effectively extracted,the attention mechanism is used to reorganize the emotional word vector to capture the relationship between non-contiguous words in the word vector.Constructing a deep learning model of ACNN and Bi-GRU based on attention mechanism.ACNN is used for sentence synthesis.The Bi-GRU model based on attention mechanism is used to synthesize documents to extract rich document features.On the movie review datasets,compared with the FastText classifier and other models,the experimental results show that the proposed document-level sentiment analysis method has much better performance than the traditional neural network method.(3)In view of the fact that the traditional method of sentiment analysis for document-level lacks the attention to the context of the text,the constructed S-LDA theme model is used to extract the topic features,At the same time,the topic features and text features are integrated into the proposed deep learning model by pre-integrated way to fully consider the context of the text.The document-level sentiment analysis model of the proposed deep learning model incorporating the topic features is compared with the related research such as TextHFT.The research finds that the model has higher recall rate and F value.