Polarity Detection and Aspect-Based Sentiment Analysis of E-Commerce Product Reviews

Abstract

With the rapid growth of e-commerce platforms, customer reviews have become an essential source of informa- tion for both consumers and businesses. However, extracting meaningful insights from large volumes of textual data is a challenging task. Traditional sentiment analysis methods provide only an overall opinion, which fails to capture detailed feedback about specific product features. This paper presents an aspect- based sentiment analysis system that identifies product aspects and determines sentiment for each aspect individually. The system uses Natural Language Processing techniques along with a transformer-based model to improve accuracy and contextual understanding. The proposed approach provides detailed insights that help businesses improve products and assist customers in making informed decisions.

Introduction

This text describes a system for aspect-based sentiment analysis of e-commerce product reviews aimed at overcoming the limitations of traditional sentiment analysis methods.

The motivation comes from the rapid growth of platforms like Amazon and Flipkart, where large volumes of customer reviews influence buying decisions. Traditional sentiment analysis typically classifies an entire review as positive, negative, or neutral, but fails to capture fine-grained opinions about specific product features. For example, a user may like a phone’s design but dislike its battery life, which traditional methods cannot separate effectively.

To solve this, the proposed system performs aspect-based sentiment analysis (ABSA), where reviews are broken down into specific product features (such as battery, camera, or display), and sentiment is assigned to each feature individually. This provides more detailed and useful insights for both customers and businesses.

The system uses a modular pipeline: it collects reviews from e-commerce sites using web scraping, cleans the text through preprocessing, extracts aspects using spaCy-based NLP techniques, and then classifies sentiment using a transformer-based DistilBERT model. The results are displayed through an interactive web interface with visualizations like charts and tables.

The architecture is designed to be scalable and maintainable, with separate modules for data collection, preprocessing, aspect extraction, sentiment classification, and visualization. The frontend is built using HTML, CSS, JavaScript, and Bootstrap, while the backend is implemented in Python.

Overall, the system enables more accurate and structured sentiment analysis, helping users understand product strengths and weaknesses in detail, while also assisting businesses in analyzing customer feedback more effectively.

Conclusion

This paper presents an aspect-based sentiment analysis system that provides detailed insights into customer reviews. The use of advanced NLP techniques and transformer-based models improves accuracy and performance. The system helps both businesses and customers by provid- ing meaningful and structured information. It can be further improved to handle more complex scenarios in the future. This paper presented a comprehensive approach to aspect- based sentiment analysis for e-commerce product reviews. The system focuses on extracting meaningful insights from unstructured textual data by identifying product features and analyzing sentiment at a granular level. The use of Natural Language Processing techniques com- bined with transformer-based models enables the system to achieve high accuracy and better contextual understanding compared to traditional methods. The proposed approach not only improves the quality of sentiment analysis but also provides valuable insights for both customers and businesses. Customers benefit from detailed product evaluations, while businesses gain a deeper under- standing of user feedback. The modular design of the system ensures flexibility and allows for future enhancements. With continuous advance- ments in machine learning and natural language processing, the system can be further improved to handle more complex scenarios. Overall, the proposed system demonstrates the potential of advanced sentiment analysis techniques in transforming the way customer feedback is analyzed and utilized.

References

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Copyright

Copyright © 2026 Prof. Vidya Yengur, Krishna Mundada, Sarthak More, Omkar Kute. 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.