Base on contextual phrases with cross-correlation attention for aspect-level sentiment analysis

In recent years, sentiment analysis has emerged as a prominent area of research within the field of natural language processing. Particularly, aspect-level sentiment classification has gained significant attention for its focus on discerning and analyzing sentiment expressed towards specific aspects within sentences. Existing methods primarily rely on extracting keywords from sentence contexts to determine sentiment polarity, yielding satisfactory results. However, a notable limitation of these approaches is their inability to consider the crucial information contained within key phrases in sentences, which plays a vital role in sentiment analysis. To address this limitation, we propose a novel deformable convolutional network model designed to leverage the power of phrases for aspect-level sentiment analysis. By utilizing deformable convolutions with adaptive receptive fields, our model effectively extracts phrase representations at various contextual distances. Furthermore, a cross-correlation attention mechanism is incorporated to capture interdependencies between phrases and words in the context. To evaluate the effectiveness of our approach, we conduct comprehensive evaluations across widely used datasets, demonstrating the promising performance of our model in enhancing sentiment classification tasks. Our model outperforms the model based on CNN, which also leverages phrase extraction, by improving accuracy by 1.71%, 2.5%, and 1.89%, respectively, on the Laptop, Restaurant, and Twitter datasets. Additionally, it surpasses the performance of the latest models.