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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Arun Biradar
REVA University
Computer Science & IT Engineering

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A hybrid model for enhanced aspect-based sentiment analysis using large language models Mohammed Ziaulla; Arun Biradar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1825-1838

Abstract

Aspect-based sentiment analysis (ABSA) is a crucial task within natural language processing (NLP), enabling fine-grained opinion mining by identifying sentiments associated with specific aspects of a product or service. While transformer-based models like bidirectional encoder representations from transformers (BERT) have improved sentiment classification, they still struggle with limited contextual adaptability, especially in customer reviews containing complex expressions. Most existing approaches rely heavily on benchmark datasets such as semantic evaluation (SemEval) and multi-aspect multi-sentiment (MAMS), which do not fully capture the diversity of real-world review scenarios. Hence, this research addresses these limitations by proposing a novel hybrid model, called as hybrid-BERT (H-BERT), that integrates span-aware BERT (SpanBERT) with bidirectional long short-term memory (BiLSTM), conditional random field (CRF), and large language models (LLMs). The objective is to enhance aspect extraction and sentiment classification performance using both annotated and synthetic data. The methodology includes preprocessing, hybrid model training, and evaluation using the SemEval 2014 dataset. Experimental results show that H-BERT achieved 90.58% accuracy and 90.56% F-score in the laptop domain and 91.21% accuracy with a 92.03% F-score in the restaurant domain. These results outperform existing models, confirming H-BERT’s robustness and effectiveness. In conclusion, H-BERT improves sentiment understanding in customer reviews.
An explicit multi-aspect sentiment analysis using bidirectional encoder representation transformers Mohammed Ziaulla; Arun Biradar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4839-4851

Abstract

Aspect-based sentiment analysis (ABSA) is a vital natural language processing (NLP) task that extends traditional sentiment analysis by identifying sentiments associated with specific aspects within a sentence. Although recent deep learning (DL) models have improved ABSA performance, most concentrate on extracting only a single aspect and predicting sentence-level polarity, thereby overlooking multiple explicit aspects and their individual sentiments. This reveals a research gap in fine-grained sentiment detection. The main problem addressed in this work is the absence of robust models capable of extracting multiple explicit aspects and accurately predicting aspect-wise polarity. To address this, the primary objective was to design a model supporting multi-aspect extraction and sentiment classification. Accordingly, this work proposes explicit aspect-aware bidirectional encoder representations from transformers (EA-BERT), a BERT-based architecture enhanced with a multi-head attention (MHA) mechanism to capture aspect-specific contextual features and predict their corresponding polarities. The model was trained and evaluated on the SemEval 2014 restaurant (Res2014) and laptop (Lap2014) datasets. EA-BERT achieved accuracies of 90.86% and 90.23% and macro-F-scores of 91.89% and 90.28%, respectively. Experimental comparisons demonstrate that EA-BERT consistently outperforms several recent ABSA models, confirming its effectiveness for multi-aspect sentiment classification.
Co-Authors Mohammed Ziaulla