DESS: DeBERTa Enhanced Syntactic-Semantic Aspect Sentiment Triplet Extraction

2025-11-13

AI Summary

In fine-grained sentiment analysis, Aspect-Sentiment-Triple Extraction (ASTE) suffers from difficulties in modeling complex interdependencies among aspects, opinion terms, and sentiment polarities—particularly under long-range dependencies and nested structures. To address this, we propose a DeBERTa-LSTM dual-channel architecture: DeBERTa serves as the backbone for deep semantic representation, while two parallel LSTM channels explicitly model syntactic structure and sequential dependencies, respectively; a semantic–syntactic interaction mechanism is further introduced to enhance cross-channel feature fusion. This design significantly improves modeling of both intra-triple relations and inter-component associations. Evaluated on multiple standard ASTE benchmarks, our method achieves absolute F1-score improvements of 4.85, 8.36, and 2.42 percentage points, respectively, demonstrating superior robustness and accuracy over state-of-the-art approaches.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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.

Problem

• Improving aspect-opinion pair extraction accuracy
• Enhancing sentiment polarity determination in complex sentences
• Integrating advanced language models for better contextual understanding

Innovation

• Integrates DeBERTa's enhanced attention mechanism for context
• Combines DeBERTa with LSTM in dual-channel structure
• Refines interaction between semantic and syntactic information processing