Fine-grained sentiment analysis faces ongoing challenges in Aspect Sentiment Triple Extraction (ASTE), particularly in accurately capturing the relationships between aspects, opinions, and sentiment polarities. While researchers have made progress using BERT and Graph Neural Networks, the full potential of advanced language models in understanding complex language patterns remains unexplored. We introduce DESS, a new approach that builds upon previous work by integrating DeBERTa's enhanced attention mechanism to better understand context and relationships in text. Our framework maintains a dual-channel structure, where DeBERTa works alongside an LSTM channel to process both meaning and grammatical patterns in text. We have carefully refined how these components work together, paying special attention to how different types of language information interact. When we tested DESS on standard datasets, it showed meaningful improvements over current methods, with F1-score increases of 4.85, 8.36, and 2.42 in identifying aspect opinion pairs and determining sentiment accurately. Looking deeper into the results, we found that DeBERTa's sophisticated attention system helps DESS handle complicated sentence structures better, especially when important words are far apart. Our findings suggest that upgrading to more advanced language models when thoughtfully integrated, can lead to real improvements in how well we can analyze sentiments in text. The implementation of our approach is publicly available at: https://github.com/VishalRepos/DESS.

翻译：细粒度情感分析在方面情感三元组抽取（ASTE）任务中持续面临挑战，特别是在准确捕捉方面、观点与情感极性之间的关联方面。尽管研究者已利用BERT和图神经网络取得进展，但先进语言模型在理解复杂语言模式方面的潜力仍未得到充分挖掘。本文提出DESS方法，该方案在先前研究基础上整合DeBERTa增强的注意力机制，以更精准地理解文本中的上下文与关联关系。我们的框架保持双通道结构：DeBERTa与LSTM通道协同工作，分别处理文本的语义信息与语法模式。我们精细优化了这些组件的协同机制，特别关注不同类型语言信息的交互作用。在标准数据集上的实验表明，DESS在识别方面-观点对及准确判定情感极性方面较现有方法取得显著提升，F1分数分别提高4.85、8.36和2.42。深入分析发现，DeBERTa精密的注意力系统使DESS能更好地处理复杂句法结构，尤其在关键词语相距较远时表现突出。研究结果表明，通过精心整合更先进的语言模型，可实质性提升文本情感分析的性能。本方法实现已公开于：https://github.com/VishalRepos/DESS。