مدل سازی در مهندسی

مدل سازی در مهندسی

تحلیل احساسات مبتنی‌بر جنبه روی نظرات کاربران در یک بازارگاه برخط

نوع مقاله : مقاله پژوهشی

نویسندگان
دانشکده مهندسی برق و کامپیوتر، گروه مهندسی کامپیوتر، دانشگاه یزد، یزد، ایران
چکیده
در سال‌های اخیر، با توجه به گسترش خریدهای اینترنتی و اهمیت بازخورد کاربران در بهبود کیفیت محصولات و خدمات، تحلیل احساسات روی نظرهای کاربران به یکی از مهم‌ترین ابزارها و فرصت‌های پیش‌روی کسب‌وکارها و خدمات برخط تبدیل شده است. در این پژوهش رویکردهای مختلف مبتنی‌بر شبکه عصبی پیچشی، مدل‌ زبانی برت و همچنین مدل‌ زبانی فست‌تکست برای تحلیل احساسات نظرهای کاربران در مورد تلفن همراه در جنبه‌های دوربین، باتری و قیمت در یک بازارگاه اینترنتی بررسی شده است. در همین راستا، داده‌ها مبتنی‌بر جنبه و بر اساس سه احساس مثبت، منفی و خنثی برچسب‌گذاری شد. همچنین برای بهبود عملکرد رویکردهای پیشنهادی و عدم توازن بین داده‌ها، از روش‌های افزایش داده‌ها استفاده و تأثیر آن‌ها بررسی شده است. در نهایت با استفاده از مدل‌های پیشنهادی، دقت بسیار بالایی در تشخیص نظرهای مثبت، منفی و خنثی در هر جنبه به‌دست آمد. بر اساس نتایج، رویکرد پیشنهادی مبتنی‌بر شبکه عصبی پیچشی، عملکرد بهتری نسبت به دو روش پیشنهادی دیگر روی داده‌های مورد نظر داشته است.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Aspect-based Sentiment Analysis based on Users' Comments in an Online Marketplace

نویسندگان English

Fatemeh Moodi
Amir Jahangard-Rafsanjani
Fatemeh Sadri
Department of Computer Engineering, Yazd University, Yazd, Iran
چکیده English

In recent years, with the expansion of online shopping and the importance of user feedback in improving the quality of products and services, sentiment analysis of user reviews has become one of the most important tools and opportunities for online businesses and services. In this research, various approaches based on Convolutional Neural Networks (CNN), BERT language model, and FastText language model have been examined for sentiment analysis of user reviews about mobile phones in aspects such as camera, battery, and price in an online marketplace. In this regard, the data were labeled based on aspects and categorized into three sentiments: positive, negative, and neutral. Additionally, to improve the performance of the proposed approaches and address data imbalance, data augmentation methods were utilized and their impact was analyzed. Finally, using the proposed models, very high accuracy in detecting positive, negative, and neutral reviews in each aspect was achieved. According to the results, the proposed CNN-based approach performed better than the other two proposed methods on the given data.

کلیدواژه‌ها English

Sentiment analysis
FastText
Different aspects of comments
Deep learning
Convolutional neural network
BERT
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دوره 24، شماره 84
بهار 1405
صفحه 27-46

  • تاریخ دریافت 09 اردیبهشت 1403
  • تاریخ بازنگری 03 فروردین 1404
  • تاریخ پذیرش 11 خرداد 1404