Aspect category sentiment analysis based on multiple attention mechanisms and pre-trained models

The rapid rise of e-commerce platforms has changed people's shopping habits, driving the popularity of online shopping. Users express their opinions on products and services by purchasing products on platforms and posting comments. These comment data contain rich user experiences, which are crucial for enterprises to understand user needs and improve product quality. Sentiment analysis of comment text is an important research direction in text mining, focusing on how to extract user evaluations of products from comment data to provide comprehensive, authentic, and accurate product feedback. This paper mainly investigates aspect-based fine-grained sentiment analysis. Because users express multiple sentiments towards different aspects in comments, coarse-grained sentiment analysis cannot accurately capture users' sentiment tendencies. This study utilizes artificial intelligence technology based on deep learning, firstly, it constructs auxiliary training samples to transform aspect sentiment tasks into machine reading comprehension or language inference tasks, using BERT model to extract text features and sentence features from comment data. To guide the model to focus on the features most relevant to the given aspect, a cross-attention mechanism is utilized to cross-focus the features of text with aspect category features. Finally, the sentiment polarity of given aspect category text can be predicted through a forward network. Experimental results on multiple datasets demonstrate that this method outperforms other deep learning models.