Aspect-Level Sentiment Analysis Approach via BERT and Aspect Feature Location Model

With the rapid development of Internet social platforms, buyer shows (such as comment text) have become an important basis for consumers to understand products and purchase decisions. The early sentiment analysis methods were mainly text-level and sentence-level, which believed that a text had only one sentiment. This phenomenon will cover up the details, and it is difficult to reflect people's fine-grained and comprehensive sentiments fully, leading to people's wrong decisions. Obviously, aspect-level sentiment analysis can obtain a more comprehensive sentiment classification by mining the sentiment tendencies of different aspects in the comment text. However, the existing aspect-level sentiment analysis methods mainly focus on attention mechanism and recurrent neural network. They lack emotional sensitivity to the position of aspect words and tend to ignore long-term dependencies. In order to solve this problem, on the basis of Bidirectional Encoder Representations from Transformers (BERT), this paper proposes an effective aspect-level sentiment analysis approach (ALM-BERT) by constructing an aspect feature location model. Specifically, we use the pretrained BERT model first to mine more aspect-level auxiliary information from the comment context. Secondly, for the sake of learning the expression features of aspect words and the interactive information of aspect words' context, we construct an aspect-based sentiment feature extraction method. Finally, we construct evaluation experiments on three benchmark datasets. The experimental results show that the aspect-level sentiment analysis performance of the ALM-BERT approach proposed in this paper is significantly better than other comparison methods.