Research on Aspect-Level Sentiment Analysis of User Reviews

Abstract

Aspect-based sentiment analysis has become one of the hot research directions of natural language processing. Compared with the traditional sentiment analysis technology, aspect-based sentiment analysis is aimed at specific targets in sentences, and can judge the sentiment tendency of multiple targets in a sentence, and more accurately mine the sentiment polarity of the target. It is a fine-grained sentiment analysis technology. Aiming at the fact that the previous research ignored the problem of separate modeling of targets, an interactive attention network model based on bidirectional long short-term memory (Bi-IAN) is proposed. The model uses bidirectional long short-term memory (BiLSTM) to model the targets and the context respectively, to obtain hidden representation and extract the semantic information. Next, the attention vector between the context and the targets is learnt through interactive learning, and then the representation of the target and the context are generated. The relevance within and between the target and the context is captured, the representation of the target and context is reconstructed, and finally the model gets the classification result through the non-linear layer. Experimental training on the dataset SemEval 2014 task 4 and Chinese review datasets shows that the model proposed has better results than the existing benchmark sentiment analysis model in terms of accuracy and F1-score.

Keywords

• aspect-level sentiment analysis
• deep learning
• recurrent neural network (rnn)
• attention mechanism