Aspect Based Sentiment Analysis for Product Reviews

Abstract

Customerreviewsareveryimportantine-commerce since they influence purchasers’ decisions to buy. Traditional sentiment analysis gives reviews polarities, but it ignores how people feel about certain aspects of the product. Finding the relevant features, such as battery, camera, and pricing, as wellas the sentiment expressed for each aspect, is the goal of aspect- based sentiment analysis, or ABSA. Lexicon-based methods, machine learning approaches, and deep learning tactics aresomeofthemethodologiesforABSAthatwillbeexaminedin this study. To extract characteristics and correctly classify feelings, we use techniques from Natural Language Processing (NLP), such as Named Entity Recognition (NER), dependency parsing, and trained models like BERT. Benchmark data is used to evaluate the proposed model, showing how well it provides deeper sentiment insights. The findings of this study can be used to improve recommendation systems, examine product reviews, and assist companies in precisely understanding the preferences of their clients. Results from this study can be used to improve recommendationsystems,evaluateconsumerfeedback,andassist companies in better understanding the preferences of their clients.

Introduction

Overview

The ABSA-One model is a comprehensive deep learning-based framework designed to enhance Aspect-Based Sentiment Analysis (ABSA) by integrating transformer architectures (like BERT), named entity recognition, dependency parsing, and hybrid methods. It was rigorously tested on benchmark datasets and real-world product reviews and demonstrated superior performance in aspect extraction, sentiment classification, sarcasm detection, and computational efficiency compared to traditional models (e.g., Naïve Bayes, SVM, LSTM).

Key Results

• Aspect Extraction: Achieved 90% F1-score using BERT, outperforming rule-based and LSTM models.

• Sentiment Classification: Reached 93.2% accuracy, demonstrating strong context understanding.

• Sarcasm Detection: Improved accuracy to 89.5%, outperforming baseline models by 25%.

• Scalability: Successfully processed 10,000 reviews in 3 minutes, showing promise for real-time applications.

Problem Addressed

Traditional sentiment analysis assigns overall sentiment but fails to detect aspect-specific sentiments, sarcasm, and mixed emotions. This causes inaccuracies in business insights from reviews. For example:

• “The camera is great, but the battery sucks” is misinterpreted by generic sentiment models.

• Sarcasm like “I love charging my phone five times a day” is often misclassified as positive.

Objectives & Contributions

1. Fine-Grained Sentiment Analysis: Automatically extract product aspects and their associated sentiments.

2. Improved Aspect Detection: Uses NER and dependency parsing to detect nuanced product features.

3. Contextual Sentiment Classification: Transformer models help understand the sentiment in ambiguous contexts.

4. Sarcasm and Implicit Sentiment Handling: Combines lexicons with deep learning to improve detection.

5. Domain Adaptability: Model generalizes across product categories with minimal additional training.

6. Future Enhancements: Focus on multilingual support, better sarcasm detection, and real-time analytics.

Challenges Identified

• Aspect Variability: Users describe the same features using different terms.

• Context Sensitivity: Sentiment depends on how words are used in context.

• Sarcasm & Implicit Emotion: Difficult to detect with traditional models.

• Scalability: Many models require extensive labeled data and do not scale across domains.

• Computational Demand: Transformer models are resource-intensive and need GPU/TPU for efficiency.

• Privacy & Ethics: Concerns around user data and bias in sentiment classification.

Design Constraints

• High Computational Load: Transformer models are demanding in terms of memory and time.

• Limited Labeled Data: Quality datasets are scarce across domains.

• Latency: Real-time sentiment analysis demands low-latency solutions.

• Interpretability: Sentiment models need to be transparent and explainable.

• Integration Issues: Models must work smoothly with existing analytics or logistics systems.

Literature Review Highlights

• Traditional Methods: Rule-based, Naïve Bayes, SVMs had low accuracy and poor sarcasm/context handling.

• Neural Models: LSTM, CNN, attention mechanisms improved performance, especially in sentiment granularity.

• Transformer Models: BERT and its variants revolutionized ABSA with contextualized embeddings.

• Memory Networks & GRU: Allowed aspect-aware representation and better interpretability.

• Multilingual ABSA: Emerging field using cross-lingual embeddings for non-English sentiment analysis.

• Datasets: SemEval (2014–2016) and IMDB are key resources for benchmarking.

Conclusion

The developments in this study clearly establish the legitimate powers of aspect-based sentiment analyses toward conversions for actionable business intelligence, an entire reviewclassificationthatwehavedemonstratedwithour end-to-end system processing raw customer feedback through advanced computational techniques. The study showed thatby theoretically analyzing the mixes of rule-based dependencyparsingandtopicmodelingtechniques,alongsideneural network-based entity recognition systems, certain aspects of the products analyzed will matter most to consumers. The perspective of the system also provided a comparison of the different approaches of sentiment classification from lexicon- based, conventional machine learning algorithms to the most currentdeeplearningarchitectures,andtheyendedupshowing that transformer-based architectures, such as BERT, would be expected to perform the best in expressing nuanced ways of decoding customer sentiment; the older conventions were still usefulforsomescenarios.Thiswouldrealizethevisualization frameworkwebuilttoconvertcomplexsentimentpatternsinto moreintuitivegraphicalrepresentationsforquickidentificationofexistingstrengths,weaknesses,andeventualtrendsinproduct perception, which closes the gap between advanced analyses and their utilization in business practice. Challenges stay still ahead in the landscape, with implicit aspects detection, sarcasm interpretation, and comprehension of contextual nuances always being among the hurdles that slow the potential of automated sentiment analysis. Future work should reflect upon reinforcement learning paradigms, multimodal analysis withsonicandvisualdata,andmorehighlydevelopedcontext- awaremodels.Applicationsofourfindings,besidesproductre- views,couldextendtoanyothersectorinhealthcare,financial services, and hospitality, whereby indicating improvementsof strategic value with knowledge of salient aspects around customer experience. Thus, yet another fruitful direction for integration of ABSA and recommendation systems opening areas within personalized customer experiences.

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Copyright

Copyright © 2025 Nikita Kumari, Amit Kumar Jaiswal, Md Sahil Saifi, Harpreet Singh, Atul Raj, Mayur Rastogi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.