Aspect-based sentiment analysis via bidirectional variant spiking neural P systems

In recent years, aspect-based sentiment classification has predominantly relied on artificial neural networks, achieving notable success. However, effectively capturing the contextual semantic relationships between aspects and content has remained challenging. To address this issue, we propose an innovative approach for aspect-based sentiment analysis. Our method uses a Bidirectional Variant Spiking Neural P System (Bi-VSNP) as a context encoding module, integrated with a two-channel mechanism to extract context and aspect features. Furthermore, Our dual attention mechanism leverages the relationship between aspects and context, allowing the model to focus on key sentiment information. We rigorously evaluated our method on publicly available datasets, demonstrating its superior performance over most models based on artificial neural networks as context extraction. Compared to other models utilizing SNP methods, our approach achieves performance improvements of 1.72%, 1.27%, and 1.05% on the Laptop, Restaurant, and Twitter datasets, respectively. These results underscore the efficacy of our approach in aspect-based sentiment classification. •The LSTM-SNP network is modified as the context extraction module.•The Bi-VSNP is designed to better capture the long-term dependencies.•The dual attention module to capture context and aspect relationships.