An AI-Enabled Platform for Authentic Vehicle Review Analysis and Intelligent Comparison

Abstract

The rapid growth of the automotive industry and user-generated content has changed how consumers evaluate vehicles, but existing review platforms are often fragmented and unreliable. This paper proposes a Vehicle Review System, a centralized and intelligent platform that collects, analyses, and presents authentic vehicle reviews. The system uses AI, Machine Learning, and NLP to perform sentiment analysis and feature-based opinion mining on user feedback. Vehicles are evaluated using key factors such as mileage, comfort, safety, maintenance cost, and fuel efficiency, converting subjective opinions into quantitative insights. A Decision Tree model supports vehicle comparison, ranking, and recommendations, while review verification mechanisms filter spam and biased inputs. Developed with modern web technologies, the system is scalable, responsive, and user-friendly. The proposed framework enhances transparency, supports informed decision-making for consumers, and helps manufacturers understand market trends and product performance.

Introduction

The text discusses the growing complexity of vehicle purchasing decisions in today’s rapidly evolving automobile market and the limitations of existing online review platforms. While digital reviews are widely used, many systems suffer from fake or biased content, unstructured information, and inconsistent ratings, which reduce trust and make objective vehicle comparison difficult. Advances in Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) provide effective solutions by transforming unstructured user reviews into reliable, data-driven insights through sentiment analysis, feature extraction, spam detection, and intelligent recommendations.

The literature review highlights extensive research on AI-based vehicle review systems, including sentiment classification, fake review detection, aspect-based opinion mining, recommendation systems, multilingual analysis, and transformer-based models such as BERT. These studies demonstrate that AI techniques significantly improve review authenticity, interpretability, personalization, and decision-making accuracy compared to traditional methods.

Addressing the identified gaps, the proposed AI-driven Vehicle Review System is designed as a multi-layered architecture that integrates data acquisition, preprocessing, AI analytics, and user-centric presentation. The system authenticates reviews, detects spam, analyses sentiments, and evaluates vehicles based on key parameters such as mileage, comfort, safety, performance, and maintenance cost. It leverages ML models and the Gemini AI API to generate structured comparisons and human-readable summaries, delivered through a secure, interactive web interface supported by a FastAPI backend.

Overall, the system aims to enhance transparency, trust, and usability in the automotive marketplace by providing reliable reviews, intelligent vehicle comparisons, and personalized recommendations. By combining AI-powered analytics, robust security, and continuous feedback mechanisms, the proposed platform supports informed consumer decision-making and fosters greater confidence in digital vehicle review ecosystems.

Conclusion

The Car and Bike Comparison System provide an efficient and user-friendly platform for comparing two vehicles based on various parameters such as price, specifications, features, and performance. It helps users make informed decisions by presenting detailed and organized information in one place. The system simplifies the comparison process, saving time and effort for potential buyers. It can be used by individuals, automobile enthusiasts, and dealers to analyse vehicle differences effectively. Overall, the project successfully demonstrates how technology can enhance the vehicle selection process and improve user experience.

References

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Copyright

Copyright © 2025 Prasad S R, Sangamesh V N, Spandan S Gowda, Tejas G M, Suraj D. 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.