An Improved BERT and Syntactic Dependency Representation Model for Sentiment Analysis

Abstract

Text representation of social media is an important task for users' sentiment analysis. Utilizing the better representation, we can accurately acquire the real semantic information expressed by online users. However, existing works cannot achieve the best results. In this paper, we construct and implement a sentiment analysis model based on the improved BERT and syntactic dependency. Firstly, by studying the word embeddings of BERT, we have ameliorated the embeddings representation. Attention mechanism is added to the word embeddings, sentence embeddings, and position embeddings. Secondly, we have exploited the dependency syntax analysis of the text, and the dependency relationship of different syntactic components will be obtained. For different syntactic components, the hierarchical attention mechanism is used to construct the phrase embeddings or block embeddings. Finally, we splice the syntactic blocks for sentiment analysis. Extensive experiments show that the proposed model has a stronger ability than the baselines on two standard data sets.