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Sentiment Analysis: Amazon Electronics Reviews Using BERT and Textblob
Mahgoub, Abdulrahman Atef, Hesham Nasser, Abdulrahman Yasser, Mohamed Medhat, Walaa M. Darweesh, M. Saeed
Mahgoub, Abdulrahman
Atef, Hesham
Nasser, Abdulrahman
Yasser, Mohamed
Medhat, Walaa M.
Darweesh, M. Saeed
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Supervisor
Date
2023-01
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Abstract
The market needs a deeper and more comprehensive grasp of its insight, where the analytics world and methodologies such as “Sentiment Analysis” come in. These methods can assist people especially “business owners” in gaining live insights into their businesses and determining wheatear customers are satisfied or not. This paper plans to provide indicators by gathering real world Amazon reviews from Egyptian customers. By applying both Bidirectional Encoder Representations from Transformers “Bert” and “Text Blob” sentiment analysis methods. The processes shall determine the overall satisfaction of Egyptian customers in the electronics department - in order to focus on a specific domain. The two methods will be compared for both the Arabic and English languages. The results show that people in Amazon.eg are mostly satisfied with the percentage of 47%. For the performance, BERT outperformed Textblob indicating that word embedding model BERT is more superior than rule-based model Textblob with a difference of 15% - 25%.