ENHANCING ASPECT-BASED SENTIMENT ANALYSIS USING LARGE LANGUAGE MODELS AND DEPENDENCY PARSING

Abstract

Sentiment analysis is a critical task in Natural Language Processing (NLP) that determines sentiment polarity within textual data. Traditional sentiment analysis primarily focuses on binary classification. However, real-world reviews and social media content often exhibit multiple sentiments within a single sentence. This complexity necessitates Aspect-Based Sentiment Analysis (ABSA), which identifies aspect terms and their corresponding sentiments. Despite advancements, existing ABSA models struggle to capture interdependencies between aspect-opinion pairs, leading to misclassifications in multi-aspect scenarios.  To address this, our study proposes enhanced ABSA model which integrates dependency parsing with Large Language Model (LLM)-based learning to incorporate structured semantic knowledge for effective aspect-opinion relationship extraction. The integration of structured feature engineering and domain-specific vocabulary filtering in the proposed work ensures more precise sentiment classification. Experimental evaluations, based on average metrics computed from 5-fold cross validation, demonstrate that the proposed model outperforms existing methods. The model achieves a 3.4% improvement in precision, a 4.9% increase in recall, and a 3.8% boost in F1-score. Additionally, it yields a 5.6% increase in Matthews Correlation Coefficient (MCC), reduces the False Discovery Rate by 3.3%, and lowers the Hamming Loss by 1.7%, ensuring enhanced consistency in multi-aspect sentiment classification. These findings underscore the value of integrating structured semantic knowledge into ABSA, which can significantly enhance the accuracy of sentiment analysis in practical applications.