Improving Cross-Domain Aspect-Based Sentiment Analysis using Bert-BiLSTM Model and Dual Attention Mechanism

Data across different domains can be influenced by variations in language styles and expressions, making it challenging to migrate specialized words, particularly when focusing on aspectual words. This complexity poses difficulties in conducting cross-domain aspect- based sentiment analysis. The article begins by introducing BERT for generating word vectors as representations of training texts, enhancing text semantics in the word vector representation stage. To capture more nuanced interaction information and context-related details, the paper proposes the Bert-BiLSTM model with a dual attention mechanism(BBDAM), which divides the original input sequence into three parts: above, aspectual words, and below. A dual attention mechanism was used to assess the interaction of aspect words with the three aspects (above, below, and neighboring words) in the three discourse segments. This mechanism allows for the comprehensive extraction of interaction information. By comparing with other modeling approaches, the experimental results show that the BB-DAM model produces good results in fine-grained cross-domain sentiment analysis.