Multi-refined Feature Enhanced Sentiment Analysis Using Contextual Instruction

2025-11-01

AI Summary

Addressing three key challenges in fine-grained sentiment analysis—semantic ambiguity, poor cross-domain transferability, and severe label distribution imbalance—this paper proposes a deep learning framework integrating context-aware instruction guidance, semantic enhancement, and multi-level feature extraction. Specifically, we introduce a domain-aware contextual instruction mechanism to resolve sentiment ambiguity; design a scale-adaptive deep encoder and an emotion-assessment contextual encoder to strengthen multi-granularity semantic modeling; and employ sentiment-consistent back-translation to improve data robustness. Evaluated on IMDb, Yelp, Twitter, and Amazon datasets, our method achieves accuracy improvements of 4.6%, 6.5%, 30.3%, and 4.1%, respectively, significantly outperforming state-of-the-art approaches. These results demonstrate the model’s superior generalizability, sentiment sensitivity, and domain adaptability.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Application Domains: Humanities & Computational Social Science

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Sentiment analysis using deep learning and pre-trained language models (PLMs) has gained significant traction due to their ability to capture rich contextual representations. However, existing approaches often underperform in scenarios involving nuanced emotional cues, domain shifts, and imbalanced sentiment distributions. We argue that these limitations stem from inadequate semantic grounding, poor generalization to diverse linguistic patterns, and biases toward dominant sentiment classes. To overcome these challenges, we propose CISEA-MRFE, a novel PLM-based framework integrating Contextual Instruction (CI), Semantic Enhancement Augmentation (SEA), and Multi-Refined Feature Extraction (MRFE). CI injects domain-aware directives to guide sentiment disambiguation; SEA improves robustness through sentiment-consistent paraphrastic augmentation; and MRFE combines a Scale-Adaptive Depthwise Encoder (SADE) for multi-scale feature specialization with an Emotion Evaluator Context Encoder (EECE) for affect-aware sequence modeling. Experimental results on four benchmark datasets demonstrate that CISEA-MRFE consistently outperforms strong baselines, achieving relative improvements in accuracy of up to 4.6% on IMDb, 6.5% on Yelp, 30.3% on Twitter, and 4.1% on Amazon. These results validate the effectiveness and generalization ability of our approach for sentiment classification across varied domains.

Problem

• Improving sentiment analysis for nuanced emotional cues
• Addressing domain shifts and imbalanced sentiment distributions
• Enhancing semantic grounding and generalization in sentiment classification

Innovation

• Integrates Contextual Instruction for sentiment disambiguation guidance
• Employs Semantic Enhancement Augmentation to improve model robustness
• Uses Multi-Refined Feature Extraction with specialized encoders