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    Sentiment Analysis: Amazon Electronics Reviews Using BERT and Textblob

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    Author
    ElKafrawy, Passent cc
    Mahgoub, Abdulrahman
    Atef, Hesham
    Nasser, Abdulrahman
    Yasser, Mohamed
    Medhat, Walaa M.
    Darweesh, M. Saeed
    Subject
    Sentiment analysis , Bit error rate , Transformers
    Consumer electronics , Business
    Date
    2023-01
    
    Metadata
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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%.
    Department
    Computer Science
    Publisher
    IEEE
    DOI
    https://doi.org/10.1109/ESOLEC54569.2022.10009176
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.1109/ESOLEC54569.2022.10009176
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