Aspect-Based Sentiment Analysis for E-commerce Insights

Abstract

This paper details the creation of a system for a deep analysis of e-commerce reviews, offering a more advanced alternative to traditional sentiment analysis. Conventional tools typically assign a blanket \"positive\" or \"negative\" label to an entire review, a method that overlooks the specific nuances within customer feedback. . Our work implements Aspect-Based Sentiment Analysis (ABSA), a technique that pinpoints distinct product attributes—such as \"camera,\" \"battery life,\" or \"screen quality\"—and then allocates an independent sentiment to each feature. By utilizing Natural Language Processing (NLP) and modern machine learning models like Transformers, the system produces a highly detailed map of consumer opinion.

Introduction

With the rapid growth of e-commerce, customer reviews have become an important source of information for both consumers and businesses. However, the massive number of reviews makes it difficult to manually extract useful insights. Traditional sentiment analysis methods classify reviews only as positive, negative, or neutral, which often hides important details. For example, a review may praise a phone’s camera but criticize its battery life, yet a single sentiment label cannot capture these mixed opinions.

To overcome this limitation, the study proposes using Aspect-Based Sentiment Analysis (ABSA), a more advanced text analysis approach that identifies specific product aspects and determines the sentiment associated with each aspect. This provides deeper insights into customer feedback. Traditional machine learning models such as Naive Bayes, Support Vector Machines (SVM), and Logistic Regression can perform basic sentiment classification, but they struggle with complex language patterns. More advanced deep learning models like LSTM, RNN, and transformer-based models (BERT, DistilBERT) offer better contextual understanding and higher accuracy. Lexicon-based approaches such as TextBlob and VADER are faster but often miss contextual meaning.

The proposed methodology begins with data collection and preprocessing, where product reviews are scraped from e-commerce websites and cleaned by removing HTML tags, special characters, and unnecessary text while applying normalization and lemmatization. The next step is aspect extraction, which identifies important product features such as “battery” or “screen quality.” This can be done using rule-based NLP methods or by training a Named Entity Recognition (NER) model based on transformer architectures.

After extracting aspects, aspect-specific sentiment classification is performed to determine whether each product feature has a positive, negative, or neutral sentiment. The system then integrates the results through a backend API and a frontend dashboard, where users can visualize aspect–sentiment pairs using charts and tables, enabling businesses to gain actionable insights from customer feedback.

The project is divided into four modules: data acquisition and preprocessing, aspect extraction, sentiment classification, and visualization through an API-based dashboard. Various tools and technologies such as BeautifulSoup, Scrapy, Pandas, NumPy, NLTK, spaCy, Hugging Face Transformers, TensorFlow/PyTorch, and Scikit-learn are used for implementation.

The research aims to improve upon previous systems by achieving around 85% accuracy while providing detailed insights into specific product features. Unlike traditional sentiment analysis, the ABSA approach helps businesses understand which product features customers like or dislike, making it more useful for product improvement and decision-making.

Conclusion

This project successfully moves beyond the limitations of traditional sentiment analysis by providing deep, granular insights from customer reviews. The proposed system dissects product reviews to extract specific features (aspects) and assign sentiment to each one, offering a detailed, feature-level analysis. The system\'s modular architecture, encompassing data acquisition, aspect extraction, sentiment classification, and a user-friendly frontend dashboard, provides a robust solution for a complex problem. This enables businesses to make data-driven decisions to improve their products and marketing strategies. It also empowers consumers to make smarter purchasing choices by quickly understanding the specific pros and cons of a product. The project\'s design allows for future improvements, such as adding support for more languages or detecting sarcasm, ensuring its adaptability and long-term value.

References

[1] Aspect-based sentiment classification of user reviews to understand customer satisfaction of e-commerce platforms — L. Davoodi, J. Mezei, M. Heikkilä (2025). [2] Explainable Aspect-Based Sentiment Analysis Using Attention + SHAP — W. Toms (2025, SSRNpreprint). [3] Fake review detection in e-Commerce platforms using Aspect-Based Sentiment Analysis (FakeDetectionGCN) — P. Phukon et al. (2025, MDPI / Applied Sciences). [4] AI-Driven Sentiment Analytics: Unlocking Business Value in the E-Commerce Landscape — Q. Wu, C. Xia, S. Tian (2025, arXiv). [5] Leveraging Deep Neural Networks for Aspect-Based Sentiment Classification (EEGCN) — C. Li, D. Cheng, Y. Morimoto (2025, arXiv). [6] Aspect-Based Sentiment Analysis on E-commerce Reviews using BiGRU and Bi-Directional Attention Flow (BiGRU+BiDAF) — (Apr 2025 / ResearchGate / local journals). [7] A Study On Improving Aspect-Based Sentiment Analysis — a generative instruction-tuned framework — (2024, arXiv). [8] A systematic review of aspect-based sentiment analysis — Y. C. Hua et al. (2024, Springer). [9] Aspect-Based Sentiment Analysis of Clothing Reviews in Vietnamese — P. Quoc-Hung et al. (PACLIC 2024 / ACL Anthology). [10] Aspect based sentiment analysis of consumer reviews using an Unsupervised Attention Deep Neural Framework — A. Dey et al. (Dec 2024 / Applied Soft Computing or similar).

Copyright

Copyright © 2026 Prof. Madhavi Kulkarni, Akash Shinde , Pankaj Mane, Sanket Sonar , Shubham Jadhav. 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.