Akademik Veri Yönetim Sistemi
28th International Conference on Discovery Science, DS 2025, Ljubljana, Slovenya, 23 - 25 Eylül 2025, cilt.16090 LNCS, ss.78-93, (Tam Metin Bildiri)
In today’s digital world, user-generated reviews represent invaluable insights reflecting authentic experiences, preferences, and perceptions regarding products and services. Such reviews play a critical role both for consumers seeking informed purchasing decisions and businesses aiming to optimize their offerings and strategies. Recent advancements in machine learning, particularly the emergence of Large Language Models (LLMs) have significantly enhanced the processing and interpretation of this rich, unstructured textual data. Traditional platforms for comparing products and services focus primarily on structured specifications, often neglecting the detailed, experience-based information contained in user reviews. To address this limitation, we propose a novel framework that leverages Generative AI and advanced LLMs to extract and interpret user feedback, enabling more informed, experience-aware comparisons. Our approach involves three main phases: targeted review and metadata collection; topic modeling and sentiment classification using fine-tuned BERT models; and structured comparisons powered by user reviews, featuring attribute-level scores and natural language explanations generated by advanced GenAI tools such as GPT-4. Evaluated on real-world scenarios, including comparisons of similar Amazon products and nearby hotels, our framework outperforms traditional aggregation methods by generating more precise comparative scores and context-aware explanations.