Fine-grained Sentiment Analysis of Social Network Platform of University Libraries Based on CNN-BiLSTM-HAN Hybrid Neural Network

Abstract

[Purpose/Significance] From the perspective of data mining of user comments on a social network platform of a university library, the sentiment polarity of user comments is analyzed in a fine-grained way. It provides scientific basis for a university library to understand the real sentiment tendency of its users and improve its service quality. [Method/Process] This paper takes the Chinese comments data of social network platform users of domestic university libraries as the research object. Through the TensorFlow deep learning framework, we used Keras artificial neural network library, combined convolution neural network and bidirectional long short term memory network, introduced hierarchical attention mechanism, and constructed sentiment analysis model based on CNN-BiLSTM-HAN hybrid neural network. [Results/Conclusions] The experiment is carried on by using the data set of user comments on the real social network platform of a university library. The accuracy of this method is 93.38%, and the results show that the model is effective. The model is more complex, as a result, the training time of the model is longer, the universality of the method model needs to be further tested., Emoticons are not used effectively, and the parameter setting needs further study.

Keywords

• university library|social network platform|convolution neural network|bidirectional long short term memory|hierarchical attention mechanism|sentiment analysis