Show simple item record

dc.contributor.authorElKafrawy, Passent
dc.contributor.authorMahgoub, Abdulrahman
dc.contributor.authorAtef, Hesham
dc.contributor.authorNasser, Abdulrahman
dc.contributor.authorYasser, Mohamed
dc.contributor.authorMedhat, Walaa M.
dc.contributor.authorDarweesh, M. Saeed
dc.date.accessioned2023-03-22T06:27:37Z
dc.date.available2023-03-22T06:27:37Z
dc.date.issued2023-01
dc.identifier.citationA. Mahgoub et al., "Sentiment Analysis: Amazon Electronics Reviews Using BERT and Textblob," 2022 20th International Conference on Language Engineering (ESOLEC), Cairo, Egypt, 2022, pp. 6-10,en_US
dc.identifier.doihttps://doi.org/10.1109/ESOLEC54569.2022.10009176en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/704
dc.description.abstractThe 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%.en_US
dc.publisherIEEEen_US
dc.subjectSentiment analysis , Bit error rate , Transformersen_US
dc.subjectConsumer electronics , Businessen_US
dc.titleSentiment Analysis: Amazon Electronics Reviews Using BERT and Textbloben_US
refterms.dateFOA2023-03-23T02:47:37Z
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labArtificial Intelligence & Cyber Security Laben_US
dc.subject.KSAICTen_US
dc.source.indexScopusen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.firstauthorMahgoub, Abdulrahman
dc.conference.locationEgypt, Cairoen_US
dc.conference.name20th International Conference on Language Engineering (ESOLEC) 2022en_US
dc.conference.date2022-10-12


Files in this item

Thumbnail
Name:
CSCI-467-July_28 .pdf
Size:
372.1Kb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record