Contextualized Cross-Domain Aspect Sentiment Transformer: A Fine-Grained Aspect-Centric Approach for Enhanced Context-Aware Sentiment Analysis

Context-aware sentiment analysis (CASA) is increasingly critical due to the complex nature of sentiments in digital communication. Traditional sentiment analysis often fails to capture the nuances in context-rich environments, facing challenges like disentangling sentiments, adapting to dynamic contexts, and handling cross-domain variations. Additionally, data sparsity and subjectivity in sentiment interpretation complicate CASA. To address these challenges, this paper proposed a Contextualized Cross-Domain Aspect Sentiment Transformer Network (CC-ASTN) that integrates BERT-based embeddings with aspect-specific embeddings for nuanced contextual and aspect-specific sentiment details. A core feature of CC-ASTN is its fine-grained sentiment analysis, which begins with word-level analysis to discern subtle emotional cues and modifiers, enabling the model to detect sentiment nuances. A novel dual attention mechanism dynamically adjusts focus based on relevance, resolving ambiguities. Advanced domain adversarial training and transfer learning techniques ensure effective cross-domain adaptation, while data augmentation and few-shot learning strategies tackle data sparsity. A hierarchical approach for sentiment analysis breaks down complex sentiments into granular components. The model's robustness is enhanced through dropout, layer normalization, and noise contrastive estimation (NCE), ensuring stability and performance consistency. A composite loss function balances multiple objectives, facilitating precise, domain-neutral sentiment analysis. Additionally, the model integrates real-time feedback mechanisms and leverages a multi-modal approach by incorporating textual, visual, and contextual data for holistic analysis. The CC-ASTN model demonstrates significant efficiency, with training typically taking ˜5 h. Experimental results validate the model's effectiveness, showing significant improvements over existing methods on the SemEval2014 Task 4 and SentiHood datasets. The model achieves inference times of ˜2 s, highlighting its suitability for real-time applications. These findings underscore CC-ASTN's efficacy as an advanced solution for context-aware sentiment analysis, capturing sentiment variations and aspect-level nuances with high precision and efficiency. Its adaptability to rapidly changing trends and real-time feedback integration enhance its applicability in dynamic, real-world scenarios, making it an effective tool for sentiment analysis across a range of fields.