关键词: 情感分析, 自然语言理解, 准注意力机制, 上下文注意

Abstract: Aspect-based sentiment analysis aims to predict the sentiment polarity of a specific aspect in a sentence or document. Most of recent research is to use attention mechanism to model the global. But there is a problem in that the context information needs to be considered according to different contextual contexts when the BERT model is used to calculate the dependencies between representations to extract features by sentiment classification models, which leads to the lack of contextual knowledge of the modelled features. And the importance of aspect words needs to be given more attention to affect the overall classification performance of the model. To address the problems above, this paper proposes a DFLGA-BERT (Dual Features Local-Global Attention model with BERT) in response to this problem. Local and global feature extraction modules are designed respectively to fully capture the semantic association between aspect words and context. Moreover, an improved quasi-attention mechanism is used in DFLGA-BERT, which leads the model to use minus attention in the fusion of attention to weaken the effect of noise on classification in the text. The feature fusion structure of local and global features is designed to integrate better regional and international features based on CLN (Conditional Layer Normalization). Experiments were conducted on the SentiHood and SemEval 2014 Task 4 datasets. Experimental results show that the proposed model significantly improved compared to the baselines after incorporating contextual features.

Key words: Sentiment Analysis, Natural Language Understanding, Quasi-Attention Mechanism, Context Attention