Paramita, Adi Suryaputra, and Jusak, Jusak (2025) Fine-Grained Sentiment Analysis Approach on Customer Reviews Based on Aspect-Level Emotion Detection. Journal of Applied Data Sciences, 6 (3). pp. 2235-2247.

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Abstract

In the era of digital platforms, customer reviews constitute a vital resource for understanding user sentiment and perceptiontoward products and services. Traditional sentiment analysis methods predominantly operate at the document or sentence level, often missing fine-grained emotional cues tied to specific product or service aspects. To address this limitation, this study proposes a novel Fine-Grained Sentiment Analysis (FGSA) framework that performs aspect-level sentiment classification using a joint learning approach. The proposed model employs a hybrid deep learning architecture that integrates transformer-based contextual encoders with Bidirectional Long Short-Term Memory (Bi-LSTM) layers. This design allows the model to capture both rich contextual semantics and sequential dependencies(a combination that has not been widely adopted in existing FGSA research). Additionally, we introduce a new annotated dataset of 5,000 customer reviews spanning multiple domains (electronics, food and beverages, and general services), enabling robust training and evaluation. Experimental results show that the model outperforms standard baselines, achieving an F1-score of 82.0% for aspect extraction and an accuracy of 79.8% for sentiment classification. Further analysis reveals consistent patterns, such as positive sentiments linked to design and quality, and negative sentiments associated with customer service and delivery. These insights highlight the practical value of aspect-level sentiment modelling.The key contribution of this work is the integration of a transformer-Bi-LSTM joint architecture for aspect-based sentiment analysis, supported by a domain-diverse benchmark dataset. This framework enhances the interpretability and granularity of sentiment insights and sets a foundation for future research in multilingual and multimodal contexts.

Item ID:	89028
Item Type:	Article (Research - C1)
ISSN:	2723-6471
Keywords:	Fine-Grained Sentiment Analysis, Aspect-Level Emotion Detection, Aspect-Based Sentiment Analysis, Hybrid Deep Learning
Copyright Information:	© Authors retain all copyrights. This is an open access article under the CC-BY license (https://creativecommons.org/licenses/by/4.0/).
Date Deposited:	15 Jul 2026 01:46
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460501 Data engineering and data science @ 50% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 50%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220402 Applied computing @ 50% 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 50%
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