تحلیل نظرات کاربران در فروشگاه دیجیکالا با هدف تشخیص نظرات فریبنده

چکیده مقاله :

این پژوهش به بررسی و تحلیل نظرات کاربران در فروشگاه دیجیکالا با هدف تشخیص نظرات فریبنده پرداخته است. ابتدا داده‌های نظرات کاربران جمع‌آوری و پیش‌پردازش شدند و سپس با استفاده از مدل‌های مختلف یادگیری ماشین و مدل‌های زبانی بزرگ، نظرات فریبنده تشخیص داده شدند. نتایج نشان داد که نظرات فریبنده معمولاً توسط کاربرانی با اعتبار پایین‌تر نوشته شده‌اند و نظراتی که تعداد دیسلایک بیشتری دریافت کرده‌اند، اغلب دارای اعتبار کمتری هستند. همچنین، نظرات مثبت بیشترین تعداد را دارند و کاربران با نظرات مثبت اغلب لایک‌های بیشتری دریافت می‌کنند. این پژوهش نشان داد که استفاده از مدل‌های زبانی بزرگ و یادگیری ماشین می‌تواند به بهبود تشخیص نظرات فریبنده و افزایش دقت سیستم‌های نظارت بر نظرات کاربران کمک کند و به شناسایی بهتر کاربران با ارزش و تاثیرگذار یاری رساند.

چکیده انگلیسی :

This research investigates and analyzes user reviews on the Digikala platform with the aim of detecting deceptive opinions. Initially, user review data was collected and preprocessed, followed by the application of various machine learning models and large language models to identify deceptive reviews. The results indicated that deceptive reviews are often written by users with lower credibility, and reviews that receive more dislikes tend to be less credible. Additionally, positive reviews are the most prevalent, and users with positive reviews generally receive more likes. This study demonstrated that employing large language models and machine learning can enhance the detection of deceptive opinions and improve the accuracy of user review monitoring systems, aiding in better identification of valuable and influential users.

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