A Novel Aspect-based Sentiment Analysis Network Model Based on Multilingual Hierarchy in Online Social Network

Abstract

Abstract In recent years, sentiment analysis based on aspects has become one of the research hotspots in the field of natural language processing. Aiming at the fact that the existing network model cannot fully obtain the interrelationship between sentences in the same comment and the long-distance dependence of specific aspects in the whole comment, a multilingual deep hierarchical model combining regional convolutional neural network and bidirectional LSTM network is proposed. The model obtains the time series relationship of different sentences in the comments through the regional CNN, and obtains the local features of the specific aspects in the sentence and the long-distance dependence in the whole comment through the hierarchical attention network. In addition, the model improves the word vector representation based on the gate mechanism to make the model completely independent of the language. Experimental results for different domain datasets in multi-language show that the proposed model achieves better classification results than the traditional deep network model, the network model combining with the attention mechanism and considering the relationship between sentences.