Document Type : Original/Review Paper
Authors
1 Department of Computer Engineering, Sharif University, Tehran, Iran
2 Department of Electrical Engineering, Faculty of Engineering, Arak University
3 Department of Computer Engineering, Faculty of Engineering, Arak University, Arak, 38156-8-8349, Iran
Abstract
The expansion of e-commerce has changed customer purchasing habits, moving them from brick-and-mortar stores to online venues. In this shift, some fundamental customer behaviors had to change because online shoppers cannot physically feel the products and rely heavily on customer reviews for evaluations. However, the lack of structured textual data poses challenges in sifting through numerous, diverse, and sometimes contradictory comments to make an informed purchasing decision. This study proposes a multi-step system for autonomously and intelligently analyzing customer comments to organize comment sections, utilizing general-purpose large language models. First, the proposed system automatically separates comments that discuss online shop services from those specifically related to products by tagging them. Then, it extracts the various product aspects discussed across all comments. Finally, these comments are categorized based on the extracted aspects. Additionally, a new labeled non-English dataset has been created as a benchmark dataset featuring tagged online-shop-related comments. The experimental results showed that the best performing model was Qwen 2.5, achieving an accuracy of 91.7 %.
Keywords
- Reviews categorization
- Aspect extraction
- Large language models
- Sentiment analysis
- Recommender systems
Main Subjects
)