نوع مقاله : مقاله کامپیوتر
نویسندگان
1 دانشکده مهندسی کامپیوتر، گروه مهندسی برق و کامپیوتر، دانشگاه یزد، یزد، ایران.
2 دانشکده مهندسی کامپیوتر دانشگاه یزد
3 دانشکده مهندسی برق و کامپیوتر، گروه مهندسی کامپیوتر، دانشگاه یزد، یزد، ایران.
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [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]
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