面向方面的情感分析是一种细粒度的情感分析，目的是识别句子中特定方面的情感极性。现有模型中存在句子和方面语义丢失以及语义提取不充分的问题，并且忽略句子中的上下文信息和方面术语的位置关系，导致情感极性识别准确率下降。因此，提出了基于RoBERTa和位置特征的多注意力方面级情感分析模型用于面向方面级别的情感分类。为验证模型的有效性，在SemEval2014餐厅14、SemEval2014笔记本电脑14、SemEval2015餐厅15、SemEval2016餐厅16数据集中进行了大量的实验，结果表明所提出模型的准确率和F₁高于比较的基准模型。

Aspect-oriented sentiment analysis is a fine-grained sentiment analysis aimed at identifying the sentiment polarity of specific aspects within a sentence. The existing suffer from sentence and aspect semantic loss, inadequate semantic extraction, and ignore contextual information and positional relationships of aspect terms in sentences, leading to a decrease in the accuracy of sentiment polarity recognition. Therefore, a multi-attention aspect-level sentiment analysis model based on RoBERTa and positional features was proposed for aspect-level sentiment analysis. To verify the effectiveness of the model, a large number of experiments were conducted on SemEval2014 Restaurant 14, SemEval2014 Laptop 14, SemEval2015 Restaurant 15, SemEval2016 Restaurant 16 datasets. The experimental results demonstrate that the proposed model achieves higher accuracy and F₁ compared to the benchmark models.